145 Arima Jobs

As a Model Validation/Audit/Review Specialist at KGS, your primary responsibility will be to conduct validation, audit, and review of credit loss forecasting models within the retail or wholesale domain, specifically focusing on IFRS9/IRB models and their PD/EAD/LGD components. In this role, you will need to understand relevant regulatory requirements, review development documentation, perform testing and benchmarking, create challenger models using SAS, R, or Python, and prepare comprehensive reports. Your duties will also include providing support for other model validation activities related to Underwriting scorecards, Credit Scoring, behavioral models, economic scenario models, and validation-related automation tasks as required. You will be responsible for assessing the conceptual soundness of models, critically evaluating testing conducted by developers, ensuring model integrity and accuracy, evaluating predictive power and robustness, and ensuring compliance with regulatory standards. Collaboration with seniors, AMs, and Managers will be essential to meet key project deliverables. You will be expected to take ownership of key deliverables, engage with Partners/Directors to understand project scope and business requirements, and coordinate with onshore and offshore teams for successful project delivery. Additionally, you will provide advice to non-audit clients on the implications of evolving provision accounting standards (IFRS9) and help them validate or develop credit risk measurement models. Qualifications for this role at KGS include an advanced degree in Mathematics, Statistics, Economics, or other analytical disciplines. Alternatively, a graduate degree along with an MBA in Finance and relevant experience or exposure will be considered. Additional certifications such as FRM or CFA are preferred. Ideal candidates should have 2-12 years of experience in Risk Management/Analytics at major banks, top-tier consulting firms like Big 4, or captives of well-known banks. Proficiency in programming languages such as Python, SAS, and R is required. Advanced skills in statistical and quantitative modeling, including various techniques like linear regression, logistic regression, ARIMA, Markov Chain, Merton Model, CHAID, and other predictive modeling methods, are essential. In-depth knowledge of regulatory requirements related to model risk management, such as SR11-7, SR15-18, PRA, and EBA guidelines, is also expected.,

Principal Data Scientist Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead/Architect is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. Key Responsibilities Architecting & Scaling Agentic AI Solutions Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. Hands-On Development & Optimization Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. Driving AI Innovation & Research Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. AI Strategy & Business Impact Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. Mentorship & Capability Building Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.

🚀 We're Hiring: Data Scientist (AI/ML | Industrial IoT | Time Series) 📍 Location: Hyderabad 🧠 Experience: 5+ Years Join our AI/ML initiative to predict industrial alarms from complex sensor data in refinery environments. You'll lead the development of predictive models using time series data, maintenance logs, and work in an Expert-in-the-Loop (EITL) setup with domain experts. 🔍 Key Responsibilities: Develop ML models for anomaly detection & alarm prediction from sensor/IoT time series data. Collaborate with domain experts to validate model outputs. Implement data preprocessing, feature engineering & scalable pipelines. Monitor model performance, drift, explainability (SHAP, confidence), and retraining. Contribute to production-grade MLOps workflows. ✅ What You Bring: 5+ yrs experience in Data Science/ML, especially with time series models (LSTM, ARIMA, Autoencoders). Proficiency in Python, ML libraries (scikit-learn, TensorFlow, PyTorch). Hands-on with IoT/sensor data in manufacturing/industrial domains. Experience with MLOps tools (MLflow, SageMaker, Kubeflow). Strong grasp of model interpretability, ETL (Pandas, PySpark, SQL), and cloud deployment. ✨ Bonus Points: Background in oil & gas, SCADA systems, maintenance logs, or industrial control systems. Experience with cloud platforms (AWS/GCP/Azure) and alarm classification standards.

Company Description The Smart Cube, a WNS company, is a trusted partner for high performing intelligence that answers critical business questions. And we work with our clients to figure out how to implement the answers, faster. Job Description Roles and ResponsibilitiesAssistant Managers are expected to understand client objectives and collaborate with the Project Lead to design appropriate analytical solutions. They should be able to translate business goals into structured deliverables with defined priorities and constraints. The role involves managing, organizing, and preparing data, conducting quality checks, and ensuring readiness for analysis.They should be proficient in applying statistical and machine learning techniques such as regression (linear/non-linear), decision trees, segmentation, time series forecasting, and algorithms like Random Forest, SVM, and ANN. Sanity checks and rigorous self-QC of all outputs, including work from junior analysts, are essential to ensure accuracy.Interpretation of results in the context of the client’s industry is necessary to generate meaningful insights. Assistant Managers should be comfortable handling client calls independently and coordinating regularly with onsite leads when applicable. They should be able to discuss specific deliverables or queries over calls or video conferences.They must manage projects from initiation through closure, ensuring timely and within-budget delivery. This includes collaborating with stakeholders to refine business needs and convert them into technical specifications, managing data teams, conducting performance evaluations, and ensuring high data quality. Effective communication between technical and business stakeholders is key to aligning expectations. Continuous improvement of analytics processes and methodologies is encouraged. The role also involves leading cross-functional teams and overseeing project timelines and deliverables.Client ManagementAssistant Managers will act as the primary point of contact for clients, maintaining strong relationships and making key decisions independently. They will participate in discussions on deliverables and guide project teams on next steps and solution approaches.Technical RequirementsCandidates must have hands-on experience connecting databases with Knime (e.g., Snowflake, SQL DB) and working with SQL concepts such as joins and unions. They should be able to read from and write to databases, utilize macros to automate tasks, and enable schedulers to run workflows. The ability to design and build ETL workflows and datasets in Knime for BI reporting tools is crucial. They must perform end-to-end data validation and maintain documentation supporting BI reports.They should be experienced in developing interactive dashboards and reports using PowerBI and leading analytics projects using PowerBI, Python, and SQL. Presenting insights clearly through PowerPoint or BI dashboards (e.g., Tableau, Qlikview) is also expected.Ideal CandidateThe ideal candidate will have 4 to 7 years of relevant experience in advanced analytics for Marketing, CRM, or Pricing within Retail or CPG; other B2C sectors may also be considered. Experience in managing and analyzing large datasets using Python, R, or SAS is required, along with the use of multiple analytics and machine learning techniques.They should be able to manage client communications independently and understand consumer-facing industries such as Retail, CPG, or Telecom. Familiarity with handling various data formats (flat files, RDBMS) and platforms (Knime, SQL Server, Teradata, Hadoop, Spark) in both on-premise and cloud environments is expected. A solid foundation in advanced statistical techniques such as regressions, decision trees, clustering, forecasting (ARIMA/X), and machine learning is essential.Other SkillsStrong verbal and written communication is a must. The candidate should be able to deliver client-ready outputs using Excel and PowerPoint. Knowledge of optimization techniques (linear/non-linear), supply chain concepts, VBA, Excel Macros, Tableau, and Qlikview is a plus. Qualifications Engineers from top tier institutes (IITs, DCE/NSIT, NITs) or Post Graduates in Maths/Statistics/OR from top Tier Colleges/UniversitiesMBA from top tier B-schools

Company Description The Smart Cube, a WNS company, is a trusted partner for high performing intelligence that answers critical business questions. And we work with our clients to figure out how to implement the answers, faster. Job Description Roles and ResponsibilitiesAssistant Managers must understand client objectives and collaborate with the Project Lead to design effective analytical frameworks. They should translate requirements into clear deliverables with defined priorities and constraints. Responsibilities include managing data preparation, performing quality checks, and ensuring analysis readiness. They should implement analytical techniques and machine learning methods such as regression, decision trees, segmentation, forecasting, and algorithms like Random Forest, SVM, and ANN.They are expected to perform sanity checks and quality control of their own work as well as that of junior analysts to ensure accuracy. The ability to interpret results in a business context and identify actionable insights is critical. Assistant Managers should handle client communications independently and interact with onsite leads, discussing deliverables and addressing queries over calls or video conferences.They are responsible for managing the entire project lifecycle from initiation to delivery, ensuring timelines and budgets are met. This includes translating business requirements into technical specifications, managing data teams, ensuring data integrity, and facilitating clear communication between business and technical stakeholders. They should lead process improvements in analytics and act as project leads for cross-functional coordination.Client ManagementThey serve as client leads, maintaining strong relationships and making key decisions. They participate in deliverable discussions and guide project teams on next steps and execution strategy.Technical RequirementsAssistant Managers must know how to connect databases with Knime (e.g., Snowflake, SQL) and understand SQL concepts such as joins and unions. They should be able to read/write data to and from databases and use macros and schedulers to automate workflows. They must design and manage Knime ETL workflows to support BI tools and ensure end-to-end data validation and documentation.Proficiency in PowerBI is required for building dashboards and supporting data-driven decision-making. They must be capable of leading analytics projects using PowerBI, Python, and SQL to generate insights. Visualizing key findings using PowerPoint or BI tools like Tableau or Qlikview is essential.Ideal CandidateCandidates should have 4–7 years of experience in advanced analytics across Marketing, CRM, or Pricing in Retail or CPG. Experience in other B2C domains is acceptable. They must be skilled in handling large datasets using Python, R, or SAS and have worked with multiple analytics or machine learning techniques. Comfort with client interactions and working independently is expected, along with a good understanding of consumer sectors such as Retail, CPG, or Telecom.They should have experience with various data formats and platforms including flat files, RDBMS, Knime workflows and server, SQL Server, Teradata, Hadoop, and Spark—on-prem or in the cloud. Basic knowledge of statistical and machine learning techniques like regression, clustering, decision trees, forecasting (e.g., ARIMA), and other ML models is required.Other SkillsStrong written and verbal communication is essential. They should be capable of creating client-ready deliverables using Excel and PowerPoint. Knowledge of optimization methods, supply chain concepts, VBA, Excel Macros, Tableau, and Qlikview will be an added advantage. Qualifications Engineers from top tier institutes (IITs, DCE/NSIT, NITs) or Post Graduates in Maths/Statistics/OR from top Tier Colleges/UniversitiesMBA from top tier B-schools

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Data Scientist AI Garage is responsible for establishing Mastercard as an AI powerhouse. AI will be leveraged and implemented at scale within Mastercard providing a foundational, competitive advantage for the future. All internal processes, all products and services will be enabled by AI continuously advancing our value proposition, consumer experience, and efficiency. Opportunity Join Mastercard's AI Garage @ Gurgaon, a newly created strategic business unit executing on identified use cases for product optimization and operational efficiency securing Mastercard's competitive advantage through all things AI. The AI professional will be responsible for the creative application and execution of AI use cases, working collaboratively with other AI professionals and business stakeholders to effectively drive the AI mandate. Role Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases Gather relevant information to define the business problem interfacing with global stakeholders Creative thinker capable of linking AI methodologies to identified business challenges Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models Develop AI/ML solutions/applications leveraging the latest industry and academic advancements Leverage open and closed source technologies to solve business problems Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda Partner with technical teams to implement developed solutions/applications in production environment Support a learning culture continuously advancing AI capabilities All About You Experience Experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: o Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL o Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning o Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil o Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing o Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Exposure or experience using collaboration tools such as: o Confluence (Documentation) o Bitbucket/Stash (Code Sharing) o Shared Folders (File Sharing) o ALM (Project Management) Knowledge of payments industry a plus Experience with SAFe (Scaled Agile Framework) process is a plus Effectiveness Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum Capable of navigating a complex organization in a relentless pursuit of answers and clarity Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment Incredible attention to detail and focus instilling confidence without qualification in developed solutions Core Capabilities Strong written and oral communication skills Strong project management skills Concentration in Computer Science Some international travel required Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Manager Data Scientist AI Garage is responsible for establishing Mastercard as an AI powerhouse. AI will be leveraged and implemented at scale within Mastercard providing a foundational, competitive advantage for the future. All internal processes, all products and services will be enabled by AI continuously advancing our value proposition, consumer experience, and efficiency. Opportunity Join Mastercard's AI Garage @ Gurgaon, a newly created strategic business unit executing on identified use cases for product optimization and operational efficiency securing Mastercard's competitive advantage through all things AI. The AI professional will be responsible for the creative application and execution of AI use cases, working collaboratively with other AI professionals and business stakeholders to effectively drive the AI mandate. Role Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases Gather relevant information to define the business problem interfacing with global stakeholders Creative thinker capable of linking AI methodologies to identified business challenges Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models Develop AI/ML solutions/applications leveraging the latest industry and academic advancements Leverage open and closed source technologies to solve business problems Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda Partner with technical teams to implement developed solutions/applications in production environment Support a learning culture continuously advancing AI capabilities All About You Experience Experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: o Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL o Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning o Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil o Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing o Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Exposure or experience using collaboration tools such as: o Confluence (Documentation) o Bitbucket/Stash (Code Sharing) o Shared Folders (File Sharing) o ALM (Project Management) Knowledge of payments industry a plus Experience with SAFe (Scaled Agile Framework) process is a plus Effectiveness Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum Capable of navigating a complex organization in a relentless pursuit of answers and clarity Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment Incredible attention to detail and focus instilling confidence without qualification in developed solutions Core Capabilities Strong written and oral communication skills Strong project management skills Concentration in Computer Science Some international travel required #AI1 Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Roles and ResponsibilitiesAssistant Managers must understand client objectives and collaborate with the Project Lead to design effective analytical frameworks. They should translate requirements into clear deliverables with defined priorities and constraints. Responsibilities include managing data preparation, performing quality checks, and ensuring analysis readiness. They should implement analytical techniques and machine learning methods such as regression, decision trees, segmentation, forecasting, and algorithms like Random Forest, SVM, and ANN.They are expected to perform sanity checks and quality control of their own work as well as that of junior analysts to ensure accuracy. The ability to interpret results in a business context and identify actionable insights is critical. Assistant Managers should handle client communications independently and interact with onsite leads, discussing deliverables and addressing queries over calls or video conferences.They are responsible for managing the entire project lifecycle from initiation to delivery, ensuring timelines and budgets are met. This includes translating business requirements into technical specifications, managing data teams, ensuring data integrity, and facilitating clear communication between business and technical stakeholders. They should lead process improvements in analytics and act as project leads for cross-functional coordination.Client ManagementThey serve as client leads, maintaining strong relationships and making key decisions. They participate in deliverable discussions and guide project teams on next steps and execution strategy.Technical RequirementsAssistant Managers must know how to connect databases with Knime (e.g., Snowflake, SQL) and understand SQL concepts such as joins and unions. They should be able to read/write data to and from databases and use macros and schedulers to automate workflows. They must design and manage Knime ETL workflows to support BI tools and ensure end-to-end data validation and documentation.Proficiency in PowerBI is required for building dashboards and supporting data-driven decision-making. They must be capable of leading analytics projects using PowerBI, Python, and SQL to generate insights. Visualizing key findings using PowerPoint or BI tools like Tableau or Qlikview is essential.Ideal CandidateCandidates should have 4–7 years of experience in advanced analytics across Marketing, CRM, or Pricing in Retail or CPG. Experience in other B2C domains is acceptable. They must be skilled in handling large datasets using Python, R, or SAS and have worked with multiple analytics or machine learning techniques. Comfort with client interactions and working independently is expected, along with a good understanding of consumer sectors such as Retail, CPG, or Telecom.They should have experience with various data formats and platforms including flat files, RDBMS, Knime workflows and server, SQL Server, Teradata, Hadoop, and Spark—on-prem or in the cloud. Basic knowledge of statistical and machine learning techniques like regression, clustering, decision trees, forecasting (e.g., ARIMA), and other ML models is required.Other SkillsStrong written and verbal communication is essential. They should be capable of creating client-ready deliverables using Excel and PowerPoint. Knowledge of optimization methods, supply chain concepts, VBA, Excel Macros, Tableau, and Qlikview will be an added advantage. Qualifications Engineers from top tier institutes (IITs, DCE/NSIT, NITs) or Post Graduates in Maths/Statistics/OR from top Tier Colleges/UniversitiesMBA from top tier B-schools Job Location

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Senior Data Scientist, Product Data & Analytics Senior Data Scientist, Product Data & Analytics Our Vision: Product Data & Analytics team builds internal analytic partnerships, strengthening focus on the health of the business, portfolio and revenue optimization opportunities, initiative tracking, new product development and Go-To Market strategies. We are a hands-on global team providing scalable end-to-end data solutions by working closely with the business. We influence decisions across Mastercard through data driven insights. We are a team on analytics engineers, data architects, BI developers, data analysts and data scientists, and fully manage our own data assets and solutions. Are you excited about Data Assets and the value they bring to an organization? Are you an evangelist for data driven decision making? Are you motivated to be part of a Global Analytics team that builds large scale Analytical Capabilities supporting end users across the continents? Are you interested in proactively looking to improve data driven decisions for a global corporation? Role Responsible for developing data-driven innovative scalable analytical solutions and identifying opportunities to support business and client needs in a quantitative manner and facilitate informed recommendations / decisions. Accountable for delivering high quality project solutions and tools within agreed upon timelines and budget parameters and conducting post- implementation reviews. Contributes to the development of custom analyses and solutions, derives insights from extracted data to solve critical business questions. Activities include developing and creating predictive models, behavioural segmentation frameworks, profitability analyses, ad hoc reporting, and data visualizations. Able to develop AI/ML capabilities, as needed on large volumes of data to support analytics and reporting needs across products, markets and services. Able to build end to end reusable, multi-purpose AI models to drive automated insights and recommendations. Leverage open and closed source technologies to solve business problems. Work closely with global & regional teams to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support analytics and reporting needs across products, markets, and services. Support initiatives in developing predictive models, behavioural segmentation frameworks, profitability analyses, ad hoc reporting, and data visualizations. Translates client/ stakeholder needs into technical analyses and/or custom solutions in collaboration with internal and external partners, derive insights and present findings and outcomes to clients/stakeholders to solve critical business questions. Create repeatable processes to support development of modelling and reporting Delegate and reviews work for junior level colleagues to ensure downstream applications and tools are not compromised or delayed. Serves as a mentor for junior-level colleagues, and develops talent via ongoing technical training, peer review etc. All About You 6-8 years of experience in data management, data mining, data analytics, data reporting, data product development and quantitative analysis. Advanced SQL skills, ability to write optimized queries for large data sets. Experience on Platforms/Environments: Cloudera Hadoop, Big data technology stack, SQL Server, Microsoft BI Stack, Cloud, Snowflake, and other relevant technologies. Data visualization tools (Tableau, Domo, and/or Power BI/similar tools) experience is a plus Experience with data validation, quality control and cleansing processes to new and existing data sources. Experience on Classical and Deep Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks - Feedforward, CNN, NLP, etc. Experience on Deep Learning algorithm techniques, open-source tools and technologies, statistical tools, and programming environments such as Python, R, and Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning. Experience in automating and creating data pipeline via tools such as Alteryx, SSIS. Nifi is a plus Financial Institution or a Payments experience a plus Additional Competencies Excellent English, quantitative, technical, and communication (oral/written) skills. Ownership of end-to-end Project Delivery/Risk Mitigation Virtual team management and manage stakeholders by influence Analytical/Problem Solving Able to prioritize and perform multiple tasks simultaneously Able to work across varying time zone. Strong attention to detail and quality Creativity/Innovation Self-motivated, operates with a sense of urgency. In depth technical knowledge, drive, and ability to learn new technologies. Must be able to interact with management, internal stakeholders Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must. Abide by Mastercard’s security policies and practices. Ensure the confidentiality and integrity of the information being accessed. Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. #AI Corporate Security Responsibility All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Software Engineer II We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. The Mastercard Launch program is aimed at early career talent, to help you develop skills and gain cross-functional work experience. Over a period of 18 months, Launch participants will be assigned to a business unit, learn and develop skills, and gain valuable on the job experience. Mastercard has over 2 billion payment cards issued by 25,000+ banks across 190+ countries and territories, amassing over 10 petabytes of data. Millions of transactions are flowing to Mastercard in real-time providing an ideal environment to apply and leverage AI at scale. The AI team is responsible for building and deploying innovative AI solutions for all divisions within Mastercard securing a competitive advantage. Our objectives include achieving operational efficiency, improving customer experience, and ensuring robust value propositions of our core products (Credit, Debit, Prepaid) and services (recommendation engine, anti-money laundering, fraud risk management, cybersecurity) Role Gather relevant information to define the business problem Creative thinker capable of linking AI methodologies to identified business challenges Develop AI/ML applications leveraging the latest industry and academic advancements Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda All About You : Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Concentration in Computer Science Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Manager Data Scientist AI Garage is responsible for establishing Mastercard as an AI powerhouse. AI will be leveraged and implemented at scale within Mastercard providing a foundational, competitive advantage for the future. All internal processes, all products and services will be enabled by AI continuously advancing our value proposition, consumer experience, and efficiency. Opportunity Join Mastercard's AI Garage @ Gurgaon, a newly created strategic business unit executing on identified use cases for product optimization and operational efficiency securing Mastercard's competitive advantage through all things AI. The AI professional will be responsible for the creative application and execution of AI use cases, working collaboratively with other AI professionals and business stakeholders to effectively drive the AI mandate. Role Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases Gather relevant information to define the business problem interfacing with global stakeholders Creative thinker capable of linking AI methodologies to identified business challenges Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models Develop AI/ML solutions/applications leveraging the latest industry and academic advancements Leverage open and closed source technologies to solve business problems Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda Partner with technical teams to implement developed solutions/applications in production environment Support a learning culture continuously advancing AI capabilities Experience All About You Experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Exposure or experience using collaboration tools such as: Confluence (Documentation) Bitbucket/Stash (Code Sharing) Shared Folders (File Sharing) ALM (Project Management) Knowledge of payments industry a plus Experience with SAFe (Scaled Agile Framework) process is a plus Effectiveness Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum Capable of navigating a complex organization in a relentless pursuit of answers and clarity Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment Incredible attention to detail and focus instilling confidence without qualification in developed solutions Core Capabilities Strong written and oral communication skills Strong project management skills Concentration in Computer Science Some international travel required #AI1 Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Senior Data Scientist We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. The Mastercard Launch program is aimed at early career talent, to help you develop skills and gain cross-functional work experience. Over a period of 18 months, Launch participants will be assigned to a business unit, learn and develop skills, and gain valuable on the job experience. Mastercard has over 2 billion payment cards issued by 25,000+ banks across 190+ countries and territories, amassing over 10 petabytes of data. Millions of transactions are flowing to Mastercard in real-time providing an ideal environment to apply and leverage AI at scale. The AI team is responsible for building and deploying innovative AI solutions for all divisions within Mastercard securing a competitive advantage. Our objectives include achieving operational efficiency, improving customer experience, and ensuring robust value propositions of our core products (Credit, Debit, Prepaid) and services (recommendation engine, anti-money laundering, fraud risk management, cybersecurity) Role Gather relevant information to define the business problem Creative thinker capable of linking AI methodologies to identified business challenges Develop AI/ML applications leveraging the latest industry and academic advancements Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda All About You : Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Concentration in Computer Science Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Senior Data Scientist AI Garage is responsible for establishing Mastercard as an AI powerhouse. AI will be leveraged and implemented at scale within Mastercard providing a foundational, competitive advantage for the future. All internal processes, all products and services will be enabled by AI continuously advancing our value proposition, consumer experience, and efficiency. Opportunity Join Mastercard's AI Garage @ Gurgaon, a newly created strategic business unit executing on identified use cases for product optimization and operational efficiency securing Mastercard's competitive advantage through all things AI. The AI professional will be responsible for the creative application and execution of AI use cases, working collaboratively with other AI professionals and business stakeholders to effectively drive the AI mandate. Role Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases Gather relevant information to define the business problem interfacing with global stakeholders Creative thinker capable of linking AI methodologies to identified business challenges Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models Develop AI/ML solutions/applications leveraging the latest industry and academic advancements Leverage open and closed source technologies to solve business problems Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda Partner with technical teams to implement developed solutions/applications in production environment Support a learning culture continuously advancing AI capabilities Experience All About You Experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Exposure or experience using collaboration tools such as: Confluence (Documentation) Bitbucket/Stash (Code Sharing) Shared Folders (File Sharing) ALM (Project Management) Knowledge of payments industry a plus Experience with SAFe (Scaled Agile Framework) process is a plus Effectiveness Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum Capable of navigating a complex organization in a relentless pursuit of answers and clarity Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment Incredible attention to detail and focus instilling confidence without qualification in developed solutions Core Capabilities Strong written and oral communication skills Strong project management skills Concentration in Computer Science Some international travel required Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Job Description - Jr. Data Scientist Experience: 0-1 year | Employment Type: Full-time Overview We are looking for a motivated Data Scientist with foundational data science expertise. This position is ideal for recent graduates or early-career professionals eager to work with real-world data, applying both standard and advanced preprocessing and modeling techniques in a collaborative environment. PLEASE NOTE: Mandatory: Email your CV to careers@solvusai.com with the subject line: “Job ID 202507-DS01: ” This is a full-time role with a hybrid work model Students currently pursuing a degree should not apply A strong foundation and clear understanding of data science concepts is essential Key Responsibilities 1. Data Ingestion & Preparation Extract and manipulate data using SQL and Python (pandas) Import and clean both structured & unstructured datasets 2. Data Preprocessing & Feature Engineering Handle missing values, outliers, and duplicates using statistical and ML techniques Apply noise reduction, data integration, and transformation (e.g., scaling, encoding) Perform dimensionality reduction (e.g., PCA) and ensure quality through data validation 3. Model Building & Evaluation Develop machine learning models (e.g., Linear Regression, Random Forest, XGBoost, ARIMA, LSTM) for varied problem types Tune hyperparameters using cross-validation and assess models using standard metrics (accuracy, RMSE, F1-score, etc.) 4. Visualization & Insight Generation Build dashboards and visualizations using tools like Matplotlib, Seaborn, Plotly, or Tableau Conduct statistical analysis to derive actionable business insights and present findings clearly 5. Team Collaboration Work closely with cross-functional teams (data engineers, analysts, business units) to align deliverables with organizational goals Participate in agile discussions and contribute to iterative development Required Skills Bachelors or Masters in Data Science, Computer Science, Statistics, or related field Proficiency in Python or R Strong SQL skills for data extraction/manipulation from relational databases Experience handling CSV/Excel data ingestion; advanced data cleaning techniques Understanding and implementation of various machine learning models (e.g., Linear Regression, Decision Tree, Random Forest, XGBoost, ARIMA, LSTM, SVM, K-Means) and their practical applications Ability to evaluate model performance using appropriate metrics: accuracy, precision, recall, F1-score, RMSE, MAE, ROC-AUC, etc Experience with feature scaling and categorical variable encoding Data visualization using Matplotlib, Seaborn, Plotly, or Tableau Analytical thinking, problem-solving, teamwork, and clear communication Ready to make an impact by transforming data into meaningful insights? Apply now!

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do? AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics. Do You Dream Big? We Need You. Job Description Job Title: Senior Data Scientist Location: Bangalore Reporting to: Senior Manager Purpose of the role This role sits at the intersection of data science and revenue growth strategy, focused on developing advanced analytical solutions to optimize pricing, trade promotions, and product mix. The candidate will lead the end-to-end design, deployment, and automation of machine learning models and statistical frameworks that support commercial decision-making, predictive scenario planning, and real-time performance tracking. By leveraging internal and external data sources—including transactional, market, and customer-level data—this role will deliver insights into price elasticity, promotional lift, channel efficiency, and category dynamics. The goal is to drive measurable improvements in gross margin, ROI on trade spend, and volume growth through data-informed strategies. Key tasks & accountabilities Design and implement price elasticity models using linear regression, log-log models, and hierarchical Bayesian frameworks to understand consumer response to pricing changes across channels and segments. Build uplift models (e.g., Linear Regression, XGBoost for treatment effect) to evaluate promotional effectiveness and isolate true incremental sales vs. base volume. Develop demand forecasting models using ARIMA, SARIMAX, and Prophet, integrating external factors such as seasonality, promotions, and competitor activity, time-series clustering and k-means segmentation to group SKUs, customers, and geographies for targeted pricing and promotion strategies. Construct assortment optimization models using conjoint analysis, choice modeling, and market basket analysis to support category planning and shelf optimization. Use Monte Carlo simulations and what-if scenario modeling to assess revenue impact under varying pricing, promo, and mix conditions. Conduct hypothesis testing (t-tests, ANOVA, chi-square) to evaluate statistical significance of pricing and promotional changes. Create LTV (lifetime value) and customer churn models to prioritize trade investment decisions and drive customer retention strategies. Integrate Nielsen, IRI, and internal POS data to build unified datasets for modeling and advanced analytics in SQL, Python (pandas, statsmodels, scikit-learn), and Azure Databricks environments. Automate reporting processes and real-time dashboards for price pack architecture (PPA), promotion performance tracking, and margin simulation using advanced Excel and Python. Lead post-event analytics using pre/post experimental designs, including difference-in-differences (DiD) methods to evaluate business interventions. Collaborate with Revenue Management, Finance, and Sales leaders to convert insights into pricing corridors, discount policies, and promotional guardrails. Translate complex statistical outputs into clear, executive-ready insights with actionable recommendations for business impact. Continuously refine model performance through feature engineering, model validation, and hyperparameter tuning to ensure accuracy and scalability. Provide mentorship to junior analysts, enhancing their skills in modeling, statistics, and commercial storytelling. Maintain documentation of model assumptions, business rules, and statistical parameters to ensure transparency and reproducibility. Other Competencies Required Presentation Skills: Effectively presenting findings and insights to stakeholders and senior leadership to drive informed decision-making. Collaboration: Working closely with cross-functional teams, including marketing, sales, and product development, to implement insights-driven strategies. Continuous Improvement: Actively seeking opportunities to enhance reporting processes and insights generation to maintain relevance and impact in a dynamic market environment. Data Scope Management: Managing the scope of data analysis, ensuring it aligns with the business objectives and insights goals. Act as a steadfast advisor to leadership, offering expert guidance on harnessing data to drive business outcomes and optimize customer experience initiatives. Serve as a catalyst for change by advocating for data-driven decision-making and cultivating a culture of continuous improvement rooted in insights gleaned from analysis. Continuously evaluate and refine reporting processes to ensure the delivery of timely, relevant, and impactful insights to leadership stakeholders while fostering an environment of ownership, collaboration, and mentorship within the team. Business Environment Main Characteristics: Work closely with Zone Revenue Management teams. Work in a fast-paced environment. Provide proactive communication to the stakeholders. This is an offshore role and requires comfort with working in a virtual environment. GCC is referred to as the offshore location. The role requires working in a collaborative manner with Zone/country business heads and GCC commercial teams. Summarize insights and recommendations to be presented back to the business. Continuously improve, automate, and optimize the process. Geographical Scope: Europe Qualifications, Experience, Skills Level of educational attainment required: Bachelor or Post-Graduate in the field of Business & Marketing, Engineering/Solution, or other equivalent degree or equivalent work experience. MBA/Engg. in a relevant technical field such as Marketing/Finance. Extensive experience solving business problems using quantitative approaches. Comfort with extracting, manipulating, and analyzing complex, high volume, high dimensionality data from varying sources. Previous Work Experience Required 5-8 years of experience in the Retail/CPG domain. Technical Skills Required Data Manipulation & Analysis: Advanced proficiency in SQL, Python (Pandas, NumPy), and Excel for structured data processing. Data Visualization: Expertise in Power BI and Tableau for building interactive dashboards and performance tracking tools. Modeling & Analytics: Hands-on experience with regression analysis, time series forecasting, and ML models using scikit-learn or XGBoost. Data Engineering Fundamentals: Knowledge of data pipelines, ETL processes, and integration of internal/external datasets for analytical readiness. Proficient in Python (pandas, scikit-learn, statsmodels), SQL, and Power BI. Skilled in regression, Bayesian modeling, uplift modeling, time-series forecasting (ARIMA, SARIMAX, Prophet), and clustering (k-means). Strong grasp of hypothesis testing, model validation, and scenario simulation. And above all of this, an undying love for beer! We dream big to create future with more cheers .

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do? AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics. Do You Dream Big? We Need You. Job Description Job Title: Senior Data Scientist Location: Bangalore Reporting to: Senior Manager Purpose of the role This role sits at the intersection of data science and revenue growth strategy, focused on developing advanced analytical solutions to optimize pricing, trade promotions, and product mix. The candidate will lead the end-to-end design, deployment, and automation of machine learning models and statistical frameworks that support commercial decision-making, predictive scenario planning, and real-time performance tracking. By leveraging internal and external data sources—including transactional, market, and customer-level data—this role will deliver insights into price elasticity, promotional lift, channel efficiency, and category dynamics. The goal is to drive measurable improvements in gross margin, ROI on trade spend, and volume growth through data-informed strategies. Key tasks & accountabilities Design and implement price elasticity models using linear regression, log-log models, and hierarchical Bayesian frameworks to understand consumer response to pricing changes across channels and segments. Build uplift models (e.g., Linear Regression, XGBoost for treatment effect) to evaluate promotional effectiveness and isolate true incremental sales vs. base volume. Develop demand forecasting models using ARIMA, SARIMAX, and Prophet, integrating external factors such as seasonality, promotions, and competitor activity, time-series clustering and k-means segmentation to group SKUs, customers, and geographies for targeted pricing and promotion strategies. Construct assortment optimization models using conjoint analysis, choice modeling, and market basket analysis to support category planning and shelf optimization. Use Monte Carlo simulations and what-if scenario modeling to assess revenue impact under varying pricing, promo, and mix conditions. Conduct hypothesis testing (t-tests, ANOVA, chi-square) to evaluate statistical significance of pricing and promotional changes. Create LTV (lifetime value) and customer churn models to prioritize trade investment decisions and drive customer retention strategies. Integrate Nielsen, IRI, and internal POS data to build unified datasets for modeling and advanced analytics in SQL, Python (pandas, statsmodels, scikit-learn), and Azure Databricks environments. Automate reporting processes and real-time dashboards for price pack architecture (PPA), promotion performance tracking, and margin simulation using advanced Excel and Python. Lead post-event analytics using pre/post experimental designs, including difference-in-differences (DiD) methods to evaluate business interventions. Collaborate with Revenue Management, Finance, and Sales leaders to convert insights into pricing corridors, discount policies, and promotional guardrails. Translate complex statistical outputs into clear, executive-ready insights with actionable recommendations for business impact. Continuously refine model performance through feature engineering, model validation, and hyperparameter tuning to ensure accuracy and scalability. Provide mentorship to junior analysts, enhancing their skills in modeling, statistics, and commercial storytelling. Maintain documentation of model assumptions, business rules, and statistical parameters to ensure transparency and reproducibility. Other Competencies Required Presentation Skills: Effectively presenting findings and insights to stakeholders and senior leadership to drive informed decision-making. Collaboration: Working closely with cross-functional teams, including marketing, sales, and product development, to implement insights-driven strategies. Continuous Improvement: Actively seeking opportunities to enhance reporting processes and insights generation to maintain relevance and impact in a dynamic market environment. Data Scope Management: Managing the scope of data analysis, ensuring it aligns with the business objectives and insights goals. Act as a steadfast advisor to leadership, offering expert guidance on harnessing data to drive business outcomes and optimize customer experience initiatives. Serve as a catalyst for change by advocating for data-driven decision-making and cultivating a culture of continuous improvement rooted in insights gleaned from analysis. Continuously evaluate and refine reporting processes to ensure the delivery of timely, relevant, and impactful insights to leadership stakeholders while fostering an environment of ownership, collaboration, and mentorship within the team. Business Environment Main Characteristics: Work closely with Zone Revenue Management teams. Work in a fast-paced environment. Provide proactive communication to the stakeholders. This is an offshore role and requires comfort with working in a virtual environment. GCC is referred to as the offshore location. The role requires working in a collaborative manner with Zone/country business heads and GCC commercial teams. Summarize insights and recommendations to be presented back to the business. Continuously improve, automate, and optimize the process. Geographical Scope: Europe 3. Qualifications, Experience, Skills Level of educational attainment required: Bachelor or Post-Graduate in the field of Business & Marketing, Engineering/Solution, or other equivalent degree or equivalent work experience. MBA/Engg. in a relevant technical field such as Marketing/Finance. Extensive experience solving business problems using quantitative approaches. Comfort with extracting, manipulating, and analyzing complex, high volume, high dimensionality data from varying sources. Previous Work Experience Required 5-8 years of experience in the Retail/CPG domain. Technical Skills Required Data Manipulation & Analysis: Advanced proficiency in SQL, Python (Pandas, NumPy), and Excel for structured data processing. Data Visualization: Expertise in Power BI and Tableau for building interactive dashboards and performance tracking tools. Modeling & Analytics: Hands-on experience with regression analysis, time series forecasting, and ML models using scikit-learn or XGBoost. Data Engineering Fundamentals: Knowledge of data pipelines, ETL processes, and integration of internal/external datasets for analytical readiness. Proficient in Python (pandas, scikit-learn, statsmodels), SQL, and Power BI. Skilled in regression, Bayesian modeling, uplift modeling, time-series forecasting (ARIMA, SARIMAX, Prophet), and clustering (k-means). Strong grasp of hypothesis testing, model validation, and scenario simulation. And above all of this, an undying love for beer! We dream big to create future with more cheers .

Company Description WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees. Job Description Job Overview: Responsible for leading data science initiatives, developing advanced analytics models and ensuring successful execution of data-driven projects for clients in the retail. Will work closely with key client stakeholders to understand their business challenges and leverage data science to deliver actionable insights that drive business growth and efficiency. Lead the design, development and implementation of advanced analytics models. Including predictive and prescriptive models for retail clients.Should be able to convert mathematical/ statistics-based research into sustainable data science solutions Candidate should be able to think from first principles to define & evangelize solutions for any client business problem Leverage deep knowledge of the retail to develop data-driven solutions that address industry-specific challenges. Apply AI/ML statistical methods to solve complex business problems and determine new opportunities for clients. Ensure project delivery of high-quality, actionable insights that drive business decisions and outcomes. Ensure end-to-end lifecycle (scoping to Delivery) of data science projects. Collaborate with cross-functional teams to ensure seamless project execution.Manage timelines, resources, and deliverables to meet client expectations and project goals. Drive innovation by exploring new data science techniques, tools, and technologies that can enhance the value delivered to clients. Strong hands-on experience with data science tools and technologies (e.g., Python, R, SQL, machine learning frameworks). Hand-on experience with a range of data science models including regression, classification, clustering, decision tree, random forest, support vector machine, naïve Bayes, GBM, XGBoost, multiple linear regression, logistic regression, and ARIMA/ARIMAX. Should be competent in Python (Pandas, NumPy, scikit-learn etc.), possess high levels of analytical skills and have experience in the creation and/or evaluation of predictive models. Preferred experience in areas such as time series analysis, market mix modelling, attribution modelling, churn modelling, market basket analysis, etc Good communication and project management skills. Should be able to communicate effectively to a wide range of audiences, both technical and business. Adept in creating Presentations, reports etc to present the analysis findings to key client stakeholders. Strong team management skills with a passion for mentoring and developing talent. Qualifications Educational Qualification:BTech/Masters in Statistics/Mathematics/Economics/Econometrics from Tier 1-2 institutions Or BE/B-Tech, MCA or MBA

Role & responsibilities Job Title: Data Scientist (3-5 Years Experience) Key Responsibilities: Develop and implement machine learning and deep learning models for various business problems, with a strong focus on time series forecasting. Analyze large, complex datasets to extract actionable insights and identify trends, patterns, and opportunities for improvement. Design, build, and validate predictive models using state-of-the-art techniques, ensuring scalability and robustness. Collaborate with cross-functional teams (Product, Engineering, Business) to translate business requirements into data science solutions. Communicate findings and recommendations clearly to both technical and non-technical stakeholders. Stay updated with the latest research and advancements in machine learning, deep learning, and time series analysis, and proactively apply new techniques as appropriate. Mentor junior team members and contribute to a culture of continuous learning and innovation. Requirements Required Skills & Qualifications: 3-5 years of hands-on experience in data science, machine learning, and statistical modeling. Strong expertise in time series forecasting (ARIMA, XGBoost, RandomForest, TFT, NHITS, etc.) and familiarity with deep learning frameworks (TensorFlow, PyTorch). Excellent programming skills in Python (preferred), with proficiency in libraries such as NumPy, Pandas, scikit-learn, and visualization tools (Matplotlib, Seaborn, Plotly). Solid conceptual understanding of machine learning algorithms, deep learning architectures, and statistical methods. Experience with data preprocessing, feature engineering, and model evaluation. Ability to learn quickly and adapt to new technologies, tools, and methodologies. Strong problem-solving skills and a keen attention to detail. Excellent communication and presentation skills. Preferred Qualifications: Experience with cloud platforms and MLOps tools. Exposure to big data technologies (Spark, Hadoop) is a plus. Masters degree in Computer Science, Statistics, Mathematics, or a related field. Benefits What a Consulting role at Thoucentric will offer you? Opportunity to define your career path and not as enforced by a manager A great consulting environment with a chance to work with Fortune 500 companies and startups alike. A dynamic but relaxed and supportive working environment that encourages personal development. Be part of One Extended Family. We bond beyond work - sports, get-togethers, common interests etc. Work in a very enriching environment with Open Culture, Flat Organization and Excellent Peer Group. Be part of the exciting Growth Story of Thoucentric!

Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________

Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________

The Senior Data Science Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. Key Responsibilities: 1. Architecting & Scaling Agentic AI Solutions: - Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. - Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. - Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization: - Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. - Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. - Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research: - Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. - Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. - Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact: - Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. - Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building: - Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. - Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.,

Job Description: Forecasting Specialist with Expertise in Inventory Management We are looking for a Forecasting Specialist with a strong background in inventory management and data-driven demand forecasting . This role is focused on optimizing inventory levels and enhancing supply chain efficiency through advanced forecasting models and analytical tools. The ideal candidate will bring expertise in statistical forecasting, data analysis, and technical systems to improve retail inventory operations. Responsibilities Statistical Demand Forecasting : Utilize advanced statistical techniques (e.g., ARIMA , exponential smoothing , seasonal decomposition , regression models ) to create accurate demand forecasts and inventory plans. Inventory Optimization : Apply mathematical models like EOQ (Economic Order Quantity) , Safety Stock Calculation , and Replenishment Algorithms to optimize inventory levels and minimize holding costs. Data Analysis & Reporting : Extract and analyze large datasets from various sources, utilizing SQL and advanced analytics tools to forecast trends, detect anomalies, and ensure inventory alignment with demand. Forecasting Model Development : Continuously develop, refine, and evaluate statistical models to improve forecasting accuracy and adapt to changing consumer behavior. System Integration : Work with ERP systems (e.g., SAP , Oracle ) and integrate them with forecasting platforms (e.g., Demand Works , Forecast Pro ) to streamline inventory management processes and ensure real-time data synchronization. Automation and Scripting : Write Python or R scripts to automate data extraction, cleaning, analysis, and forecasting tasks. Data Visualization & Communication : Leverage tools like Power BI , Tableau , or Google Data Studio to build dashboards that present inventory insights and forecasting accuracy to stakeholders in an actionable format. Continuous Improvement : Apply machine learning techniques (e.g., regression models , decision trees , ensemble methods ) to enhance forecasting models and adapt to new retail trends. Key Technical Skills Statistical Tools & Forecasting : Expertise in R or Python for statistical analysis and time series forecasting. In-depth knowledge of forecasting techniques (ARIMA, Holt-Winters, Exponential Smoothing). Understanding of advanced regression and machine learning algorithms applied to demand prediction and trend analysis. SQL & Database Management Proficiency in writing complex SQL queries to extract and manipulate data from inventory and sales databases (MySQL, PostgreSQL, etc.). Experience working with relational databases and cloud-based solutions for inventory data management. Inventory Optimization Algorithms Advanced understanding of inventory models such as ABC Classification , Just-In-Time (JIT) , Reorder Point (ROP) , Safety Stock Optimization , and Vendor-Managed Inventory (VMI) . Ability to apply algorithms to dynamically adjust reorder quantities based on fluctuating demand. Data Visualization & Business Intelligence Expertise in data visualization tools like Power BI to present key performance metrics and inventory analytics to non-technical stakeholders. Ability to create interactive dashboards that display key inventory KPIs (e.g., stock levels, turnover rates, and demand forecasts). Automation & Scripting Proficient in Python (pandas, NumPy) or R for scripting and automating inventory analysis and forecasting processes. Experience with Jupyter Notebooks or RStudio for developing reproducible analysis workflows. Additional Skills Machine Learning (ML) : Knowledge of machine learning algorithms (e.g., XGBoost , Random Forests , Neural Networks ) for improving predictive accuracy in demand forecasting. Desired Qualifications Bachelor's degree in Statistics , Computer Science , Supply Chain Management , or a related field. Proven experience in a similar technical forecasting or inventory management role, preferably in a retail or e-commerce setting. Strong problem-solving skills and a keen attention to detail in data quality and accuracy. This is a high-impact, technical role where you will leverage your expertise in statistical forecasting , data analysis , and inventory optimization to drive significant improvements in retail inventory management systems. If you are passionate about using advanced data science techniques to solve complex supply chain problems, we encourage you to apply.

Job Description- We are committed to creating a workplace for the industry’s best talent. The Smart Cube (A WNS Company) is proud to be certified as a ‘Great Place to Work’ for the fifth year running. The Smart Cube is also recognized by Great Place to Work as One of India’s Best Workplaces for Women 2021. The Smart Cube, a global provider of strategic research and analytics solutions, has been rated on Analytics India Magazine’s (AIM) Penetration and Maturity Quadrant of Top Data Science Providers as a “Seasoned Vendor” 2022 report amongst the leading analytics service providers based out of India. We are listed in top 50 data science organization The Smart Cube shortlisted for two awards at the British Data Awards. Our clients include a third of the companies in the FTSE and Fortune 100, primarily in the CPG, Life Sciences, Energy, Chemicals, Industrials, Financial Services, Professional Services, and Retail sectors. Roles and responsibilities Specifically, Assistant Managers should – Understand the client objectives, and work with the Project Lead (PL) to design the analytical solution/framework. Be able to translate the client objectives / analytical plan into clear deliverables with associated priorities and constraints Organize/Prepare/Manage data and conduct quality checks to ensure that the analysis dataset is ready Explore and implement various statistical and analytical techniques (including machine learning) like linear/non-linear Regression, Decision Trees, Segmentation, time series forecasting as well as machine learning algorithms like Random Forest, SVM, ANN, etc. Conduct sanity checks of the analysis output based on reasoning and common sense, and be able to do a rigorous self QC, as well as of the work assigned to junior analysts to ensure an error free output Interpret the output in context of the client’s business and industry to identify trends and actionable insights Be able to take client calls relatively independently, and interact with onsite leads (if applicable) on a daily basis Discuss queries/certain sections of deliverable report over client calls or video conferences Oversee the entire project lifecycle, from initiation to closure, ensuring timely and within-budget delivery. Collaborate with stakeholders to gather and refine business requirements, translating them into technical specifications. Manage a team of data analysts and developers, providing guidance, mentorship, and performance evaluations. Ensure data integrity and accuracy through rigorous data validation and quality checks. Facilitate effective communication between technical teams and business stakeholders to align project goals and expectations. Drive continuous improvement initiatives to enhance data analytics processes and methodologies. Act as a project lead, coordinating cross-functional teams and managing project timelines and deliverables. Client Management Act as client lead and maintain client relationship; make independent key decisions related to client management Be a part of deliverable discussions with clients over telephonic calls, and guide the project team on the next steps and way forward Technical Requirements: Knowledge of how to connect Database with Knime e.g. snowflake, SQL db etc. along with SQL concepts like types of joins/union of data etc. Read data from a DB and write it back to a database Working of macros to avoid repetition of task, and enabling schedulers to run work flow(s) Design and develop ETL workflows and datasets in Knime to be used by the BI Reporting tool Perform end to end Data validation and prepare technical specifications and documentation for Knime workflows supporting BI reports. Develop and maintain interactive dashboards and reports using PowerBI to support data-driven decision-making. Lead and manage data analytics projects utilizing PowerBI, Python, and SQL to guide & deliver actionable business insights. Be able to succinctly visualize the findings through a PPT, a BI dashboard (Tableau, Qlikview, etc.) and highlight the key takeaways from a business perspective Ideal Candidate 4-7 years of relevant advanced analytics experience in Marketing, CRM, Pricing in either Retail, or CPG industries. Other B2C domains can be considered Experience in managing, cleaning and analyzing large datasets using tools like Python, R or SAS Experience in using multiple advanced analytics techniques or machine learning algorithms Experience in handling client calls and working independently with clients Understanding of consumer businesses such as Retail, CPG or Telecom Knowledge of working across multiple data types and files like flat files, RDBMS files; Knime workflows, Knime server, and multiple data platforms (SQL Server, Teradata, Hadoop, Spark); on premise or on the cloud Basic knowledge of advanced statistical techniques like Decision trees, different types of regressions, clustering, Forecasting (ARIMA/X), ML, etc. Other Skills Excellent communication skills (both written and oral) Ability to create client ready deliverables in Excel and PowerPoint Optimization techniques (linear, non-linear), and knowledge of supply chain VBA, Excel Macro programming, Tableau, QlikView Education Engineers from top tier institutes (IITs, DCE/NSIT, NITs) or Post Graduates in Maths/Statistics/OR from top Tier Colleges/Universities MBA from top tier B-schools In interested, please share your updated CV on kiran.meghani@wns.com or apply on https://smrtr.io/sz4-S Looking for immediate OR early joiners

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Senior Data Scientist, Product Data & Analytics Senior Data Scientist, Product Data & Analytics Our Vision: Product Data & Analytics team builds internal analytic partnerships, strengthening focus on the health of the business, portfolio and revenue optimization opportunities, initiative tracking, new product development and Go-To Market strategies. We are a hands-on global team providing scalable end-to-end data solutions by working closely with the business. We influence decisions across Mastercard through data driven insights. We are a team on analytics engineers, data architects, BI developers, data analysts and data scientists, and fully manage our own data assets and solutions. Are you excited about Data Assets and the value they bring to an organization? Are you an evangelist for data driven decision making? Are you motivated to be part of a Global Analytics team that builds large scale Analytical Capabilities supporting end users across the continents? Are you interested in proactively looking to improve data driven decisions for a global corporation? Role Responsible for developing data-driven innovative scalable analytical solutions and identifying opportunities to support business and client needs in a quantitative manner and facilitate informed recommendations / decisions. Accountable for delivering high quality project solutions and tools within agreed upon timelines and budget parameters and conducting post- implementation reviews. Contributes to the development of custom analyses and solutions, derives insights from extracted data to solve critical business questions. Activities include developing and creating predictive models, behavioural segmentation frameworks, profitability analyses, ad hoc reporting, and data visualizations. Able to develop AI/ML capabilities, as needed on large volumes of data to support analytics and reporting needs across products, markets and services. Able to build end to end reusable, multi-purpose AI models to drive automated insights and recommendations. Leverage open and closed source technologies to solve business problems. Work closely with global & regional teams to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support analytics and reporting needs across products, markets, and services. Support initiatives in developing predictive models, behavioural segmentation frameworks, profitability analyses, ad hoc reporting, and data visualizations. Translates client/ stakeholder needs into technical analyses and/or custom solutions in collaboration with internal and external partners, derive insights and present findings and outcomes to clients/stakeholders to solve critical business questions. Create repeatable processes to support development of modelling and reporting Delegate and reviews work for junior level colleagues to ensure downstream applications and tools are not compromised or delayed. Serves as a mentor for junior-level colleagues, and develops talent via ongoing technical training, peer review etc. All About You 6-8 years of experience in data management, data mining, data analytics, data reporting, data product development and quantitative analysis. Advanced SQL skills, ability to write optimized queries for large data sets. Experience on Platforms/Environments: Cloudera Hadoop, Big data technology stack, SQL Server, Microsoft BI Stack, Cloud, Snowflake, and other relevant technologies. Data visualization tools (Tableau, Domo, and/or Power BI/similar tools) experience is a plus Experience with data validation, quality control and cleansing processes to new and existing data sources. Experience on Classical and Deep Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks - Feedforward, CNN, NLP, etc. Experience on Deep Learning algorithm techniques, open-source tools and technologies, statistical tools, and programming environments such as Python, R, and Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning. Experience in automating and creating data pipeline via tools such as Alteryx, SSIS. Nifi is a plus Financial Institution or a Payments experience a plus Additional Competencies Excellent English, quantitative, technical, and communication (oral/written) skills. Ownership of end-to-end Project Delivery/Risk Mitigation Virtual team management and manage stakeholders by influence Analytical/Problem Solving Able to prioritize and perform multiple tasks simultaneously Able to work across varying time zone. Strong attention to detail and quality Creativity/Innovation Self-motivated, operates with a sense of urgency. In depth technical knowledge, drive, and ability to learn new technologies. Must be able to interact with management, internal stakeholders Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must. Abide by Mastercard’s security policies and practices. Ensure the confidentiality and integrity of the information being accessed. Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. #AI Corporate Security Responsibility All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. R-244065

Data Scientist Primary Skills Classification (Decision Trees, SVM), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Great Expectation, Evidently AI, Hypothesis Testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Probabilistic Graph Models, Python/PySpark, R/ R Studio, Regression (Linear, Logistic), SAS/SPSS, Statistical analysis and computing, Tools(KubeFlow, BentoML), T-Test, Z-Test Specialization Data Science Advanced: Data Specialist Job requirements Key Responsibilities: Develop and implement time series forecasting models to predict future trends, demand, and performance metrics. Leverage statistical methods, machine learning, and deep learning techniques for accurate forecasting. Work with large datasets from multiple sources and clean, process, and transform data for modeling purposes. Analyze historical data patterns and trends to identify key drivers of future outcomes. Perform data validation, anomaly detection, and ensure data integrity. Collaborate with business and technical teams to understand forecasting needs and ensure models align with business objectives. Communicate findings, insights, and predictions to both technical and non-technical stakeholders. Continuously optimize and improve forecasting models by experimenting with new algorithms, tools, and techniques. Use forecasting models to drive business decisions related to supply chain, demand planning, inventory, and more. Document processes, models, and code to ensure scalability and reproducibility. Key Requirements: Education: Bachelor's or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field. Experience: 5-6 years of experience in data science with a focus on time series forecasting. Proficiency in Python or R for data analysis and modeling. Strong knowledge of time series forecasting techniques such as ARIMA, SARIMA, ETS, Prophet, or LSTM. Experience with machine learning algorithms and statistical analysis. Familiarity with tools like SQL, Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Experience working with large datasets and data pipelines in a cloud-based environment (AWS, Azure, or GCP). Strong problem-solving skills and the ability to interpret complex data. Excellent communication and presentation skills to translate complex data findings into actionable insights for business teams.