285 Xgboost Jobs - Page 9

About Hakkoda Hakkoda, an IBM Company, is a modern data consultancy that empowers data driven organizations to realize the full value of the Snowflake Data Cloud. We provide consulting and managed services in data architecture, data engineering, analytics and data science. We are renowned for bringing our clients deep expertise, being easy to work with, and being an amazing place to work! We are looking for curious and creative individuals who want to be part of a fast-paced, dynamic environment, where everyone’s input and efforts are valued. We hire outstanding individuals and give them the opportunity to thrive in a collaborative atmosphere that values learning, growth, and hard work. Our team is distributed across North America, Latin America, India and Europe. If you have the desire to be a part of an exciting, challenging, and rapidly-growing Snowflake consulting services company, and if you are passionate about making a difference in this world, we would love to talk to you!. We are seeking an exceptional and highly motivated Lead Data Scientist with a PhD in Data Science, Computer Science, Applied Mathematics, Statistics, or a closely related quantitative field, to spearhead the design, development, and deployment of an automotive OEM’s next-generation Intelligent Forecast Application. This pivotal role will leverage cutting-edge machine learning, deep learning, and statistical modeling techniques to build a robust, scalable, and accurate forecasting system crucial for strategic decision-decision-making across the automotive value chain, including demand planning, production scheduling, inventory optimization, predictive maintenance, and new product introduction. The successful candidate will be a recognized expert in advanced forecasting methodologies, possess a strong foundation in data engineering and MLOps principles, and demonstrate a proven ability to translate complex research into tangible, production-ready applications within a dynamic industrial environment. This role demands not only deep technical expertise but also a visionary approach to leveraging data and AI to drive significant business impact for a leading automotive OEM. Role Description Strategic Leadership & Application Design: Lead the end-to-end design and architecture of the Intelligent Forecast Application, defining its capabilities, modularity, and integration points with existing enterprise systems (e.g., ERP, SCM, CRM). Develop a strategic roadmap for forecasting capabilities, identifying opportunities for innovation and the adoption of emerging AI/ML techniques (e.g., generative AI for scenario planning, reinforcement learning for dynamic optimization). Translate complex business requirements and automotive industry challenges into well-defined data science problems and technical specifications. Advanced Model Development & Research: Design, develop, and validate highly accurate and robust forecasting models using a variety of advanced techniques, including: Time Series Analysis: ARIMA, SARIMA, Prophet, Exponential Smoothing, State-space models. Machine Learning: Gradient Boosting (XGBoost, LightGBM), Random Forests, Support Vector Machines. Deep Learning: LSTMs, GRUs, Transformers, and other neural network architectures for complex sequential data. Probabilistic Forecasting: Quantile regression, Bayesian methods to capture uncertainty. Hierarchical & Grouped Forecasting: Managing forecasts across multiple product hierarchies, regions, and dealerships. Incorporate diverse data sources, including historical sales, market trends, economic indicators, competitor data, internal operational data (e.g., production schedules, supply chain disruptions), external events, and unstructured data. Conduct extensive exploratory data analysis (EDA) to identify patterns, anomalies, and key features influencing automotive forecasts. Stay abreast of the latest academic researchand industry advancements in forecasting, machine learning, and AI, actively evaluating and advocating for their practical application within the OEM. Application Development & Deployment (MLOps): Architect and implement scalable data pipelines for ingestion, cleaning, transformation, and feature engineering of large, complex automotive datasets. Develop robust and efficient code for model training, inference, and deployment within a production environment. Implement MLOps best practices for model versioning, monitoring, retraining, and performance management to ensure the continuous accuracy and reliability of the forecasting application. Collaborate closely with Data Engineering, Software Development, and IT Operations teams to ensure seamless integration, deployment, and maintenance of the application. Performance Evaluation & Optimization: Define and implement rigorous evaluation metrics for forecasting accuracy (e.g., MAE, RMSE, MAPE, sMAPE, wMAPE, Pinball Loss) and business impact. Perform A/B testing and comparative analyses of different models and approaches to continuously improve forecasting performance. Identify and mitigate sources of bias and uncertainty in forecasting models. Collaboration & Mentorship: Work cross-functionally with various business units (e.g., Sales, Marketing, Supply Chain, Manufacturing, Finance, Product Development) to understand their forecasting needs and integrate solutions. Communicate complex technical concepts and model insights clearly and concisely to both technical and non-technical stakeholders. Provide technical leadership and mentorship to junior data scientists and engineers, fostering a culture of innovation and continuous learning. Potentially contribute to intellectual property (patents) and present findings at internal and external conferences. Qualifications Education: PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Experience: 5+ years of hands-on experience in a Data Scientist or Machine Learning Engineer role, with a significant focus on developing and deploying advanced forecasting solutions in a production environment. Demonstrated experience designing and developing intelligent applications, not just isolated models. Experience in the automotive industry or a similar complex manufacturing/supply chain environment is highly desirable. Technical Skills: Expert proficiency in Python (Numpy, Pandas, Scikit-learn, Statsmodels) and/or R. Strong proficiency in SQL. Machine Learning/Deep Learning Frameworks: Extensive experience with TensorFlow, PyTorch, Keras, or similar deep learning libraries. Forecasting Specific Libraries: Proficiency with forecasting libraries like Prophet, Statsmodels, or specialized time series packages. Data Warehousing & Big Data Technologies: Experience with distributed computing frameworks (e.g., Apache Spark, Hadoop) and data storage solutions (e.g., Snowflake, Databricks, S3, ADLS). Cloud Platforms: Hands-on experience with at least one major cloud provider (Azure, AWS, GCP) for data science and ML deployments. MLOps: Understanding and practical experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines). Data Visualization: Proficiency with tools like Tableau, Power BI, or similar for creating compelling data stories and dashboards. Analytical Prowess: Deep understanding of statistical inference, experimental design, causal inference, and the mathematical foundations of machine learning algorithms. Problem Solving: Proven ability to analyze complex, ambiguous problems, break them down into manageable components, and devise innovative solutions. Preferred Qualifications Publications in top-tier conferences or journals related to forecasting, time series analysis, or applied machine learning. Experience with real-time forecasting systems or streaming data analytics. Familiarity with specific automotive data types (e.g., telematics, vehicle sensor data, dealership data, market sentiment). Experience with distributed version control systems (e.g., Git). Knowledge of agile development methodologies. Soft Skills Exceptional Communication: Ability to articulate complex technical concepts and insights to a diverse audience, including senior management and non-technical stakeholders. Collaboration: Strong interpersonal skills and a proven ability to work effectively within cross-functional teams. Intellectual Curiosity & Proactiveness: A passion for continuous learning, staying ahead of industry trends, and proactively identifying opportunities for improvement. Strategic Thinking: Ability to see the big picture and align technical solutions with overall business objectives. Mentorship: Desire and ability to guide and develop less experienced team members. Resilience & Adaptability: Thrive in a fast-paced, evolving environment with complex challenges. Benefits Health Insurance Paid leave Technical training and certifications Robust learning and development opportunities Incentive Toastmasters Food Program Fitness Program Referral Bonus Program Hakkoda is committed to fostering diversity, equity, and inclusion within our teams. A diverse workforce enhances our ability to serve clients and enriches our culture. We encourage candidates of all races, genders, sexual orientations, abilities, and experiences to apply, creating a workplace where everyone can succeed and thrive. Ready to take your career to the next level? 🚀 💻 Apply today👇 and join a team that’s shaping the future!! Hakkoda is an IBM subsidiary which has been acquired by IBM and will be integrated in the IBM organization. Hakkoda will be the hiring entity. By Proceeding with this application, you understand that Hakkoda will share your personal information with other IBM subsidiaries involved in your recruitment process, wherever these are located. More information on how IBM protects your personal information, including the safeguards in case of cross-border data transfer, are available here. Show more Show less

Company Description Silverpush is at the forefront of AI-powered video advertising, delivering sophisticated ad solutions that empower brands to achieve impactful campaigns within a privacy-centric environment. Operating across 30+ countries, we specialize in creating contextually relevant advertising experiences that drive genuine engagement and conversion. Our commitment to innovation and technological advancement enables us to navigate the evolving digital landscape, providing tools necessary to connect with audiences globally. We are dedicated to fostering a culture of creativity and excellence, driving the future of ad tech. Role Description This is a full-time on-site role for a Data Scientist, located in Gurgaon. The ideal candidate will combine strong technical expertise with an AI-driven mindset, leveraging both traditional data science methods and cutting-edge AI tools to drive business impact. This role requires someone who can work independently while collaborating effectively across cross-functional teams to solve complex business problems. Key Responsibilities Analyze complex datasets to identify trends, patterns, and correlations, and extract actionable insights that can inform strategic decisions. Design and build predictive models using statistical and machine learning techniques (e.g., regression, classification, XGBoost, clustering). Research and develop analysis and forecasting and optimization methods across ads performance, content performance, and live experiments. Build and maintain automated data pipelines and analytical workflows Research and prototype using cutting-edge LLM technologies and generative AI to unlock new opportunities in personalization, targeting, and automation. Stay current with emerging AI technologies and evaluate their potential application to business problems Communicate complex findings to non-technical audiences through compelling visualizations and presentations Provide strategic recommendations based on data-driven insights to influence key business decisions Design and execute A/B tests and experiments to measure impact of business initiatives Work with engineering teams to productise models and ensure scalable deployment Maintain code quality standards and follow best practices for version control and documentation Optimize model performance and monitor deployed solutions for accuracy and reliability Ideal Candidate Profile 3+ years of experience in Data Science, ideally in advertising or media-related domains. Degree in a quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Masters in DS). Deep experience working with large-scale structured and unstructured data. Strong foundation in machine learning and statistical modeling. Familiar with building and deploying models in production (basic MLOps knowledge). Comfortable with NLP and computer vision, and interested in applying LLMs to real-world use cases. Familiarity with generative AI tools and prompt engineering for analytical workflows Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders. Strong problem-solving skills and ability to work independently Technical Skills Languages & Tools : Python, PySpark, SQL ML Techniques : Regression, Classification, Clustering, Decision Trees, Random Forests, XGBoost, SVM LLM Tech : Familiarity with tools like OpenAI, Hugging Face, LangChain, and prompt engineering Data Infrastructure : ETL tools, Postgres, BigQuery/Snowflake, S3/GCP Statistical Analysis : A/B testing, experiment design, causal inference Show more Show less

Who we are Wayfair’s Advertising business is rapidly expanding, adding hundreds of millions of dollars in profits to Wayfair. We are building Sponsored Products, Display & Video Ad offerings that cater to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of customers. We are evolving our Ads Platform to empower advertisers across all sophistication levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the art Machine Learning techniques. The Advertising Optimization & Automation Science team is central to this effort. We leverage machine learning and generative AI to streamline campaign workflows, delivering impactful recommendations on budget allocation, target Return on Ad Spend (tROAS), and SKU selection. Additionally, we are developing intelligent systems for creative optimization and exploring agentic frameworks to further simplify and enhance advertiser interactions. We are looking for Machine Learning Scientists to join the Advertising Optimization & Automation Science team. In this role, you will be responsible for the development of budget, tROAS and SKU recommendations and other machine learning capabilities supporting our ads business. You will work closely with other scientists, as well as members of our internal Product and Engineering teams, to apply your engineering and machine learning skills to solve some of our most impactful and intellectually challenging problems to directly impact Wayfair’s revenue. What you’ll do Design, build, deploy and refine large-scale machine learning models and algorithmic decision-making systems that solve real-world problems for customers Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped analytical solutions Collaborate closely with various engineering, infrastructure, and machine learning platform teams to ensure adoption of best-practices in how we build and deploy scalable machine learning services Identify new opportunities and insights from the data (where can the models be improved? What is the projected ROI of a proposed modification?) Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on. What you’ll need 3+ years of industry experience with a Bachelor/ Master’s degree or minimum of 1-2 years of industry experience with PhD in Computer Science, Mathematics, Statistics, or related field. Proficiency in Python or one other high-level programming language Solid hands-on expertise deploying machine learning solutions into production Strong theoretical understanding of statistical models such as regression, clustering and machine learning algorithms such as decision trees, neural networks, etc. Strong written and verbal communication skills Intellectual curiosity and enthusiastic about continuous learning Nice to have Experience with Python machine learning ecosystem (numpy, pandas, sklearn, XGBoost, etc.) and/or Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML) Familiarity with GCP (or AWS, Azure), machine learning model development frameworks, machine learning orchestration tools (Airflow, Kubeflow or MLFlow) Experience in information retrieval, query/intent understanding, search ranking, recommender systems etc. Experience with deep learning frameworks like PyTorch, Tensorflow, etc. Show more Show less

Company Description Nykaa is a digitally native, consumer-tech company that offers a wide range of beauty, personal care and fashion products. Since its inception in 2012, Nykaa has disrupted the beauty retail market in India and captured the hearts of millions of customers. Besides offering engaging and educational content, we have diversified our offerings through other online platforms like Nykaa Fashion, Nykaa Man, and Superstore. Role: This is a full-time on-site role for a Machine Learning Engineer/Scientist in Bengaluru. As a Machine Learning Engineer/Scientist, you will design and deploy machine learning models to solve complex business problems. You will be responsible for developing and implementing statistical and machine learning algorithms, managing large datasets, and working collaboratively with cross-functional teams. You will be working on the interesting problem areas such as Personalization, Customer Growth, Demand Forecasting & Inventory Management and other DS problems. Key Skills Minimum 3 years experience Strong background in statistics, machine learning and deep learning Expertise in pattern recognition, neural networks, and ML algorithms Proficiency in statistical tools, Python programming language along with ML libraries (Scikit-Learning, XGBoost and LightGBM etc) Exposure to DL frameworks such as Keras, Tensorflow and Pytorch Have a sound understanding of modeling pipelines, ML architecture and MLOps Excellent problem-solving skills and attention to detail Ability to work collaboratively in a fast-paced environment Experience with Causal Inference and Experimentation would be an advantage Experience in Consumer tech experience would be a plus Experience on Search, Ranking, Relevance, Recommendations is highly preferred Show more Show less

Skills: Python, TensorFlow, PyTorch, Scikit-learn, NLP, Pandas, NumPy, Data Visualization, About Us We are a growing tech company based in Ahmedabad, building AI-powered enterprise applications for the BFSI sector, government bodies, and e-Auction and ProcureToPay domains. Our platforms include eAuction, FinTech Applications, and procurement automationpowered by cutting-edge AI technologies. Requirements Final year student / recent graduate (Computer Science, IT, Data Science, etc.) Solid foundation in Python programming Academic or personal project experience in any ML framework (XGBoost preferred) Familiar with or eager to learn: Vector Databases (FAISS, Weaviate) LangChain / RAG framework Scrapy or web scraping tools Pandas, Numpy, Scikit-learn, Transformers Nice to Have Exposure to OpenAI API, HuggingFace, or LLM-based projects Interest in Finanance / eAuction / Procure To Pay / e-Governance domains Git or version control understanding What We Offer Chance to work upon the Live AI projects Performance-based full-time opportunity after internship A collaborative, growth-focused work culture Show more Show less

Job Description: Position: Senior Technical Specialist – Data Science Location: Chennai Experience: 8+ Years Domain Expertise: AI/ML, Data Science, Cloud, DevOps, and MLOps Education: B.E. (ECE), MBA (Big Data Analytics). Job Summary: We are seeking an experienced Senior Technical Specialist - Data Science to lead AI/ML initiatives, design scalable data-driven solutions, and drive innovation. The ideal candidate will have strong expertise in AI/ML, data engineering, cloud technologies (Azure), and MLOps, with experience in managing teams and delivering AI-powered solutions for business challenges. Key Responsibilities: • Lead and mentor a team of Data Scientists to develop and deploy AI/ML models. • Architect end-to-end Machine Learning & AI solutions for complex business problems. • Design and implement Generative AI applications, including RAG-based chatbots using LLMs, LangChain, and Azure OpenAI. • Build, deploy, and monitor MLOps pipelines using MLFlow, Kubeflow, and Azure MLOps. • Develop predictive modeling, NLP applications, and deep learning frameworks using TensorFlow, PyTorch, and BERT. • Conduct data analysis and visualization using Power BI, Tableau, Matplotlib, and Streamlit. • Work with DevOps teams to automate ML workflows, optimize cloud infrastructure, and enhance model scalability. • Collaborate with business teams to define AI strategy, data-driven insights, and process improvements. • Research and implement the latest advancements in AI/ML to improve model accuracy and efficiency. Required Skills & Expertise: • Programming: Python, PowerShell, Bash, Perl • Machine Learning & Deep Learning: SVM, KNN, XGBoost, TensorFlow, PyTorch, LSTM • NLP & Generative AI: Spacy, BERT, LangChain, LlamaIndex, LLOps • Data Visualization: Tableau, Power BI, Matplotlib, Streamlit • Cloud & MLOps: Azure ML Studio, Azure OpenAI, Docker, Jenkins, GitHub, MLFlow, ClearML • Database & Big Data: MS-SQL, Data Preprocessing, Feature Engineering Preferred Qualifications: • Azure Data Scientist Certification • HackerRank Python & SQL Certification • Udacity NLP & Deep Learning Certifications Show more Show less

Salary : 25 to 40 LPA Exp: 4 to 8 years Location :Noida/Gurugram/Bangalore Notice: Immediate to 30 days..!! Roles & responsibilities: 5+ years exp on Python , ML and Banking model development Interact with the client to understand their requirements and communicate / brainstorm solutions, model Development: Design, build, and implement credit risk model. Contribute to how analytical approach is structured for specification of analysis Contribute insights from conclusions of analysis that integrate with initial hypothesis and business objective. Independently address complex problems 3+ years exp on ML/Python (predictive modelling) . Design, implement, test, deploy and maintain innovative data and machine learning solutions to accelerate our business. Create experiments and prototype implementations of new learning algorithms and prediction techniques Collaborate with product managers, and stockholders to design and implement software solutions for science problems Use machine learning best practices to ensure a high standard of quality for all of the team deliverables Has experience working on unstructured data ( text ): Text cleaning, TFIDF, text vectorization Hands-on experience with IFRS 9 models and regulations. Data Analysis: Analyze large datasets to identify trends and risk factors, ensuring data quality and integrity. Statistical Analysis: Utilize advanced statistical methods to build robust models, leveraging expertise in R programming. Collaboration: Work closely with data scientists, business analysts, and other stakeholders to align models with business needs. Continuous Improvement: Stay updated with the latest methodologies and tools in credit risk modeling and R programming.

Position : AI / ML Engineer Job Type : Full-Time Location : Gurgaon, Haryana, India Experience : 2 Years Industry : Information Technology Domain : Demand Forecasting in Retail/Manufacturing Job Summary We are seeking a skilled Time Series Forecasting Engineer to enhance existing Python microservices into a modular, scalable forecasting engine. The ideal candidate will have a strong statistical background, expertise in handling multi-seasonal and intermittent data, and a passion for model interpretability and real-time insights. Key Responsibilities Develop and integrate advanced time-series models: MSTL, Croston, TSB, Box-Cox. Implement rolling-origin cross-validation and hyperparameter tuning. Blend models such as ARIMA, Prophet, and XGBoost for improved accuracy. Generate SHAP-based driver insights and deliver them to a React dashboard via GraphQL. Monitor forecast performance with Prometheus and Grafana; trigger alerts based on degradation. Core Technical Skills Languages : Python (pandas, statsmodels, scikit-learn) Time Series : ARIMA, MSTL, Croston, Prophet, TSB Tools : Docker, REST API, GraphQL, Git-flow, Unit Testing Database : PostgreSQL Monitoring : Prometheus, Grafana Nice-to-Have : MLflow, ONNX, TensorFlow Probability Soft Skills Strong communication and collaboration skills Ability to explain statistical models in layman terms Proactive problem-solving attitude Comfort working cross-functionally in iterative development environments Job Type: Full-time Pay: ₹400,000.00 - ₹800,000.00 per year Application Question(s): Do you have at least 2 years of hands-on experience in Python-based time series forecasting? Have you worked in retail or manufacturing domains where demand forecasting was a core responsibility? Are you currently authorized to work in India without sponsorship? Have you implemented or used ARIMA, Prophet, or MSTL in any of your projects? Have you used Croston or TSB models for forecasting intermittent demand? Are you familiar with SHAP for model interpretability? Have you containerized a forecasting pipeline using Docker and exposed it through a REST or GraphQL API? Have you used Prometheus and Grafana to monitor model performance in production? Work Location: In person Application Deadline: 05/06/2025 Expected Start Date: 05/06/2025

Use various statistical techniques like regression and classification tests to report on potential risk areas and perform statistical deep dives to enhance analysis. Build metrics around both statistical and practical significance by building compelling analytical arguments and statistical models. Draft clear and concise reports of the results of ongoing monitoring and special assessment projects and present results to Compliance organization 4+ years' experience in statistics, data science, decision science, or a related quantitative field Masters degree required 3+ years experience with fair lending-related testing, including the following techniques: BISG algorithm Feature/variable proxy testing Shapley or related model proxy testing Statistical significance model proxy testing Classical and non-parametric statistical techniques as applicable Knowledge of consumer lending Working knowledge of machine learning models, specifically tree models like XGboost. Excellent analytical and deep dive skills using data analysis and model building. SQL/SAS/Python Show more Show less

Experience : 5.00 + years Salary : INR 8000000-10000000 / year (based on experience) Expected Notice Period : 15 Days Shift : (GMT+05:30) Asia/Kolkata (IST) Opportunity Type : Office (Gurugram) Placement Type : Full Time Permanent position(Payroll and Compliance to be managed by: Sentilink) (*Note: This is a requirement for one of Uplers' client - A US-based, funded innovative identity and risk solutions provider organization) What do you need for this opportunity? Must have skills required: Communication Skill, Fintech, Machine Learning models leverage XGBoost and PyTorch, API Building, AWS Infrastructure ( Terraform, Docker ), Experience in handling large-scale data, Python/GO, AWS, Kubernetes, PostgreSQL A US-based, funded innovative identity and risk solutions provider organization is Looking for: Role: As a Senior Software Engineer, you will be responsible for our applications and its platform. You will work with the product and engineering team to build new products and enhance our existing suite of products. You have outstanding programming skills, you are proficient in our technology stack, and you can pick up new technologies quickly as we evolve. Technologies : Golang, Python, Opensearch, PostgreSQL (RDS), Docker, AWS technologies This is an in-office position, based on Gurugram, India. Responsibilities : You are the owner of one or more large sections of our codebase. Consistently delivers large systems involving one or more teams' contribution on time at a high level of quality Shapes broad architecture; ships multiple large services, complex libraries or major pieces of infrastructure Lead the technical direction provide guidance and set the technical direction for our team, ensuring alignment with project goals and industry best practices Consistently able to reduce the complexity of projects, services, and processes in order to increase efficiency of teams Code, test, debug, document, and maintain software applications using established coding standards and methodologies Partner with product management to drive agile delivery of both existing and new products based on project requirement Ensure new software meets quality standards by writing unit and end-to-end automated tests Work with product, data, and other stakeholders to analyze usage and product metrics to inform key strategic decisions Troubleshoot, debug, and resolve product issues as they arise Work cross functionally to resolve complex customer problems Ensure platform and services meet SLA requirements; on call rotation for production issues, along with the rest of engineering Requirements: 5+ years of software development experience building enterprise or consumer facing products Experience with building API based products using python, golang, or similar technologies Excellent analytical and problem solving skills, interpersonal skills, and a sense of humor (enjoy the journey) Familiarity with: RDBMS (e.g. postgres) and ability to write efficient queries with optimal structures; Docker and AWS technologies; fintech or financial services; scrum / agile development environment Engagement Model - Direct Hire on client Payroll Shift - 11 AM to 8 PM IST Mode of work - Onsite in Gurugram Interview Process - 4 Rounds How to apply for this opportunity? Step 1: Click On Apply! And Register or Login on our portal. Step 2: Complete the Screening Form & Upload updated Resume Step 3: Increase your chances to get shortlisted & meet the client for the Interview! About Uplers: Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement. (Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well). So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you! Show more Show less

Data Scientist - Retail & E-commerce Analytics with Personalization, Campaigns & GCP/BigQuery Expertise We are looking for a skilled Data Scientist with strong expertise in Retail & E-commerce Analytics , particularly in personalization , campaign optimization , and Generative AI (GenAI) , along with hands-on experience working with Google Cloud Platform (GCP) and BigQuery . The ideal candidate will use data science methodologies and advanced machine learning techniques to drive personalized customer experiences, optimize marketing campaigns, and create innovative solutions for the retail and e-commerce business. This role will also involve working with large-scale datasets on GCP and performing high-performance analytics using BigQuery . Responsibilities E-commerce Analytics & Personalization : Develop and implement machine learning models for personalized recommendations , product search optimization , and customer segmentation to improve the online shopping experience. Analyze customer behavior data to create tailored experiences that drive engagement, conversions, and customer lifetime value. Build recommendation systems using collaborative filtering , content-based filtering , and hybrid approaches. Use predictive modeling techniques to forecast customer behavior, sales trends, and optimize inventory management. Campaign Optimization Analyze and optimize digital marketing campaigns across various channels (email, social media, display ads, etc.) using statistical analysis and A/B testing methodologies. Build predictive models to measure campaign performance, improving targeting, content, and budget allocation. Utilize customer data to create hyper-targeted campaigns that increase customer acquisition, retention, and conversion rates. Evaluate customer interactions and campaign performance to provide insights and strategies for future optimization. Generative AI (GenAI) & Innovation Use Generative AI (GenAI) techniques to dynamically generate personalized content for marketing, such as product descriptions, email content, and banner designs. Leverage Generative AI to synthesize synthetic data, enhance existing datasets, and improve model performance. Work with teams to incorporate GenAI solutions into automated customer service chatbots, personalized product recommendations, and digital content creation. Big Data Analytics With GCP & BigQuery Leverage Google Cloud Platform (GCP) for scalable data processing, machine learning, and advanced analytics. Utilize BigQuery for large-scale data querying, processing, and building data pipelines, allowing efficient data handling and analytics at scale. Optimize data workflows on GCP using tools like Cloud Storage , Cloud Functions , Cloud Dataproc , and Dataflow to ensure data is clean, reliable, and accessible for analysis. Collaborate with engineering teams to maintain and optimize data infrastructure for real-time and batch data processing in GCP. Data Analysis & Insights Perform data analysis across customer behavior, sales, and marketing datasets to uncover insights that drive business decisions. Develop interactive reports and dashboards using Google Data Studio to visualize key performance metrics and findings. Provide actionable insights on key e-commerce KPIs such as conversion rate , average order value (AOV) , customer lifetime value (CLV) , and cart abandonment rate . Collaboration & Cross-Functional Engagement Work closely with marketing, product, and technical teams to ensure that data-driven insights are used to inform business strategies and optimize retail e-commerce operations. Communicate findings and technical concepts effectively to stakeholders, ensuring they are actionable and aligned with business goals. Key Technical Skills Machine Learning & Data Science : Proficiency in Python or R for data manipulation, machine learning model development (scikit-learn, XGBoost, LightGBM), and statistical analysis. Experience building recommendation systems and personalization algorithms (e.g., collaborative filtering, content-based filtering). Familiarity with Generative AI (GenAI) technologies, including transformer models (e.g., GPT), GANs , and BERT for content generation and data augmentation. Knowledge of A/B testing and multivariate testing for campaign analysis and optimization. Big Data & Cloud Analytics Hands-on experience with Google Cloud Platform (GCP) , specifically BigQuery for large-scale data analytics and querying. Familiarity with BigQuery ML for running machine learning models directly in BigQuery. Experience working with GCP tools like Cloud Dataproc , Cloud Functions , Cloud Storage , and Dataflow to build scalable and efficient data pipelines. Expertise in SQL for data querying, analysis, and optimization of data workflows in BigQuery . E-commerce & Retail Analytics Strong understanding of e-commerce metrics such as conversion rates , AOV , CLV , and cart abandonment . Experience with analytics tools like Google Analytics , Adobe Analytics , or similar platforms for web and marketing data analysis. Data Visualization & Reporting Proficiency in data visualization tools like Tableau , Power BI , or Google Data Studio to create clear, actionable insights for business teams. Experience developing dashboards and reports that monitor KPIs and e-commerce performance. Desired Qualifications Bachelor's or Master's degree in Computer Science , Data Science , Statistics , Engineering , or related fields. 5+ years of experience in data science , machine learning , and e-commerce analytics , with a strong focus on personalization , campaign optimization , and Generative AI . Hands-on experience working with GCP and BigQuery for data analytics, processing, and machine learning at scale. Proven experience in a client-facing role or collaborating cross-functionally with product, marketing, and technical teams to deliver data-driven solutions. Strong problem-solving abilities, with the ability to analyze large datasets and turn them into actionable insights for business growth. Show more Show less

We are looking for an experienced Data Scientist with a strong background in Retail & E-commerce Analytics , particularly in integrating offline store data into analytics solutions. The ideal candidate will leverage General Analytics , AI/ML , and other advanced data science techniques to bridge the gap between online and offline retail data, enabling smarter business decisions and enhancing customer experiences across both channels. Responsibilities: Offline Store Data Analytics : Analyze and integrate offline store data (sales, foot traffic, inventory, etc.) with online data to provide a holistic view of customer behavior and sales trends. Use AI/ML models to optimize offline store performance by forecasting demand, inventory needs, and staffing levels based on historical data. Analyze the impact of offline marketing campaigns and promotions on in-store foot traffic and sales, using predictive analytics and machine learning. Develop models to predict store performance, taking into account various factors like location, weather, local events, and other external variables. Retail & E-commerce Analytics : Use machine learning algorithms and statistical analysis to understand and predict consumer behavior, both in-store and online. Build and maintain models for customer segmentation , personalized marketing , and sales forecasting for both e-commerce and brick-and-mortar stores. Identify key metrics for store performance and customer satisfaction, helping management teams optimize strategies for in-store and online experiences. AI/ML Implementation For Retail Optimization : Apply AI/ML techniques (e.g., classification , regression , clustering , time series forecasting ) to retail data, enabling actionable insights for improving product assortment, pricing, and promotions both online and offline. Develop demand forecasting models for offline stores , ensuring optimal stock levels based on predicted customer needs and sales trends. Use machine learning to enhance inventory management in offline stores by predicting inventory shortages and surplus. Data Integration & Visualization : Work with large datasets from both offline and online sources to clean, integrate, and analyze the data, ensuring data accuracy and consistency. Develop dashboards and visualizations using tools like Tableau , Power BI , or Google Data Studio to communicate key findings and business insights to stakeholders. Create reports that combine insights from offline and online channels, providing a unified view of retail operations. Campaign Performance Analysis : Use data science techniques to analyze the effectiveness of offline marketing campaigns and promotions on both in-store and online traffic, conversion rates, and sales. Evaluate customer engagement with offline campaigns, offering insights into how online and offline channels influence each other. Collaboration With Cross-Functional Teams : Work closely with retail managers, marketing teams, and IT teams to implement data-driven solutions that improve customer experience and business performance. Collaborate with product, marketing, and supply chain teams to optimize the omnichannel strategy , including aligning online and offline inventories and promotions. Key Technical Skills: Machine Learning & AI : Proficiency in Python or R for building and deploying AI/ML models such as random forests , XGBoost , SVM , and neural networks . Strong experience in applying predictive modeling , regression analysis , and time series forecasting to retail data, including demand forecasting and sales prediction. Familiarity with deep learning techniques (e.g., RNNs , LSTMs ) for more complex data patterns, if relevant. Retail & E-commerce Analytics : Experience in analyzing point-of-sale (POS) data , foot traffic data , and customer journey data for offline retail stores. Understanding of e-commerce KPIs and how they integrate with offline store performance metrics. Strong knowledge of inventory optimization , supply chain management , and how they relate to both online and offline retail operations. Big Data & Data Integration : Proficiency in handling and analyzing large datasets from multiple sources (online and offline) using SQL , NoSQL , and cloud platforms like AWS , GCP , or Azure . Ability to work with ETL processes to integrate data from multiple systems, ensuring high-quality data for analysis. Data Visualization & Reporting : Experience with Tableau , Power BI , Google Data Studio , or other visualization tools to present insights and actionable business recommendations. Strong communication skills to present complex findings to both technical and non-technical stakeholders. Statistical Analysis : Proficiency in statistical methods for hypothesis testing, segmentation analysis, and measuring the effectiveness of retail strategies and campaigns. Familiarity with advanced statistical techniques, including Bayesian methods , Monte Carlo simulations , and multivariate testing . Desired Qualifications: Bachelor’s or Master’s degree in Computer Science , Data Science , Statistics , Engineering , or a related field. 5+ years of experience in data science or analytics in the retail or e-commerce industry, with a focus on offline store performance . Proven experience in applying AI/ML models to solve real-world business problems, including demand forecasting, personalization, and campaign optimization. Familiarity with offline store data (sales, foot traffic, inventory) and how it integrates with e-commerce platforms . Strong understanding of the retail industry, particularly in optimizing performance across omnichannel environments (online and offline). 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About Ericsson Ericsson is a leading provider of telecommunications equipment and services to mobile and fixed network operators globally. Our innovative solutions empower individuals, businesses, and societies to explore their full potential in the Networked Society. We are seeking a highly skilled and experienced Data Scientist to join our dynamic team at Ericsson. As a Data Scientist, you will be responsible for leveraging advanced analytics and machine learning techniques to drive actionable insights and solutions for our telecom domain. This role requires a deep understanding of data science methodologies, strong programming skills, and proficiency in cloud-based environments. Key Responsibilities Develop and deploy machine learning models for various applications including chat-bot, XGBoost, random forest, NLP, computer vision, and generative AI. Utilize Python for data manipulation, analysis, and modeling tasks. Proficient in SQL for querying and analyzing large datasets. Experience with Docker and Kubernetes for containerization and orchestration of applications. Basic knowledge of PySpark for distributed computing and data processing. Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions. Deploy machine learning models into production environments and ensure scalability and reliability. Preferably have experience working with Google Cloud Platform (GCP) services for data storage, processing, and deployment. Qualifications Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's degree or PhD is preferred. 8-12 years of experience in data science and machine learning roles, preferably within the telecommunications or related industry. Proven experience in model development, evaluation, and deployment. Strong programming skills in Python and SQL. Familiarity with Docker, Kubernetes, and PySpark. Solid understanding of machine learning techniques and algorithms. Experience working with cloud platforms, preferably GCP. Excellent problem-solving skills and ability to work independently as well as part of a team. Strong communication and presentation skills, with the ability to explain complex analytical concepts to non-technical stakeholders. Why join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like. Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more. Primary country and city: India (IN) || Noida Req ID: 767292 Show more Show less

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 Science Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Our Team As consumer preference for digital payments continues to grow, ensuring a seamless and secure consumer experience is top of mind. Optimization Solutions team focuses on tracking of digital performance across all products and regions, understanding the factors influencing performance and the broader industry landscape. This includes delivering data-driven insights and business recommendations, engaging directly with key external stakeholders on implementing optimization solutions (new and existing), and partnering across the organization to drive alignment and ensure action is taken. 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 team that builds large-scale Analytical Capabilities supporting end users across 6 continents? Do you want to be the go-to resource for data science & analytics in the company? The Role Work closely with global optimization solutions team to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support data insights and analytical needs across products, markets, and services The candidate for this position will focus on Building solutions using Machine Learning and creating actionable insights to support product optimization and sales enablement. Prototype new algorithms, experiment, evaluate and deliver actionable insights. Drive the evolution of products with an impact focused on data science and engineering. Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models. Perform data ingestion, aggregation, and processing on high volume and high dimensionality data to drive and enable data unification and produce relevant insights. Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations. Apply knowledge of metrics, measurements, and benchmarking to complex and demanding solutions. Role Ensure that all AI solutions follow industry standards for data management and privacy, covering aspects like data collection, use, storage, access, retention, output, reporting, and quality at Mastercard. Take a practical approach to AI, simplifying complex technical requirements to align with stakeholders' needs. Collaborate with global stakeholders to gather necessary information and define business problems. Think creatively to link AI methodologies with real business challenges. Identify common use case patterns to promote scalable AI through reusable models and a microservice approach. Build AI/ML solutions using the latest advancements from industry and academia. Use both open and proprietary technologies to address business problems. Work effectively across teams and geographies, leveraging broader team expertise to achieve AI goals. Partner with technical teams to deploy solutions in production environments. Foster a culture of learning and continuous improvement in AI capabilities. All About You 8+ years in Data Science, with a focus on AI strategy, execution, and solution development from the ground up. A passion for AI, demonstrated through participation in competitions such as Kaggle. Preferred experience or familiarity with cybersecurity, fraud, and risk solutions, including: Deep learning algorithms, open-source tools, and statistical environments (Python, R, SQL). Big data platforms like Hadoop, Hive, Spark, and GPU clusters for deep learning. Classical machine learning techniques such as Logistic Regression, Decision Trees, K-Means, PCA, and Time Series models (ARIMA/ARMA). Deep learning techniques including Random Forest, GBM, Neural Networks (CNNs, LSTMs), and optimization techniques (Adam, Adagrad, etc.). Frameworks like TensorFlow, Keras, PyTorch, and XGBoost for production systems. Experience or exposure to collaboration tools like Confluence, Bitbucket, ALM, etc. Knowledge of the payments industry and experience with SAFe (Scaled Agile Framework) is a plus. Effectiveness Ability to manage assumptions and validate them with key stakeholders under tight deadlines, while keeping development on track. Strong problem-solving skills, with the ability to break down complex issues and apply the right AI techniques to solve them. Deep attention to detail, ensuring high confidence in developed solutions. Strong understanding of technical system architecture and interdependencies, anticipating challenges and proactively solving them. Core Capabilities Excellent written and verbal communication skills. Strong project management experience. Background 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. R-245889 Show more Show less

Job Title: AI Lead Engineer Location: Vapi, Gujarat Experience Required: 5+ Years Working Days: 6 Days a Week (Monday–Saturday) Industry Exposure: Manufacturing, Retail, Finance, Healthcare, life sciences or related field. Job Description: We are seeking a highly skilled and hands-on AI Lead to join our team in Vapi . The ideal candidate will have a proven track record of developing and deploying machine learning systems in real-world environments, along with the ability to lead AI projects from concept to production. You will work closely with business and technical stakeholders to drive innovation, optimize operations, and implement intelligent automation solutions. Key Responsibilities: Lead the design, development, and deployment of AI/ML models for business-critical applications. Build and implement computer vision systems (e.g., defect detection, image recognition) using frameworks like OpenCV and YOLO. Develop predictive analytics models (e.g., predictive maintenance, forecasting) using time series and machine learning algorithms such as XGBoost. Build and deploy recommendation engines and optimization models to improve operational efficiency. Establish and maintain robust MLOps pipelines using tools such as MLflow, Docker, and Jenkins. Collaborate with stakeholders across business and IT to define KPIs and deliver AI solutions aligned with organizational objectives. Integrate AI models into existing ERP or production systems using REST APIs and microservices. Mentor and guide a team of junior ML engineers and data scientists. Required Skills & Technologies: Programming Languages: Python (advanced), SQL, Bash, Java (basic) ML Frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost DevOps & MLOps Tools: Docker, FastAPI, MLflow, Jenkins, Git Data Engineering & Visualization: Pandas, Spark, Airflow, Tableau Cloud Platforms: AWS (S3, EC2, SageMaker – basic) Specializations: Computer Vision (YOLOv8, OpenCV), NLP (spaCy, Transformers), Time Series Analysis Deployment: ONNX, REST APIs, ERP System Integration Qualifications: B.Tech / M.Tech / M.Sc in Computer Science, Data Science, or related field. 6+ years of experience in AI/ML with a strong focus on product-ready deployments. Demonstrated experience leading AI/ML teams or projects. Strong problem-solving skills and the ability to communicate effectively with cross-functional teams. Domain experience in manufacturing, retail, or healthcare preferred. What We Offer: A leadership role in an innovation-driven team Exposure to end-to-end AI product development in a dynamic industry environment Opportunities to lead, innovate, and mentor Competitive salary and benefits package 6-day work culture supporting growth and accountability This is a startup environment but with good reputable company, We are looking for someone who can work Monday to Saturday and who can lead a team and generate new solutions and ideas and lead / manage the project effectively-- Please fill this given below form before applying https://forms.gle/8b3gdxzvc2JwnYfZ6 Show more Show less

Job Title: AI Lead Engineer Location: Vapi, Gujarat Experience Required: 5+ Years Working Days: 6 Days a Week (Monday–Saturday) Industry Exposure: Manufacturing, Retail, Finance, Healthcare, life sciences or related field. Job Description: We are seeking a highly skilled and hands-on AI Lead to join our team in Vapi . The ideal candidate will have a proven track record of developing and deploying machine learning systems in real-world environments, along with the ability to lead AI projects from concept to production. You will work closely with business and technical stakeholders to drive innovation, optimize operations, and implement intelligent automation solutions. Key Responsibilities: Lead the design, development, and deployment of AI/ML models for business-critical applications. Build and implement computer vision systems (e.g., defect detection, image recognition) using frameworks like OpenCV and YOLO. Develop predictive analytics models (e.g., predictive maintenance, forecasting) using time series and machine learning algorithms such as XGBoost. Build and deploy recommendation engines and optimization models to improve operational efficiency. Establish and maintain robust MLOps pipelines using tools such as MLflow, Docker, and Jenkins. Collaborate with stakeholders across business and IT to define KPIs and deliver AI solutions aligned with organizational objectives. Integrate AI models into existing ERP or production systems using REST APIs and microservices. Mentor and guide a team of junior ML engineers and data scientists. Required Skills & Technologies: Programming Languages: Python (advanced), SQL, Bash, Java (basic) ML Frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost DevOps & MLOps Tools: Docker, FastAPI, MLflow, Jenkins, Git Data Engineering & Visualization: Pandas, Spark, Airflow, Tableau Cloud Platforms: AWS (S3, EC2, SageMaker – basic) Specializations: Computer Vision (YOLOv8, OpenCV), NLP (spaCy, Transformers), Time Series Analysis Deployment: ONNX, REST APIs, ERP System Integration Qualifications: B.Tech / M.Tech / M.Sc in Computer Science, Data Science, or related field. 6+ years of experience in AI/ML with a strong focus on product-ready deployments. Demonstrated experience leading AI/ML teams or projects. Strong problem-solving skills and the ability to communicate effectively with cross-functional teams. Domain experience in manufacturing, retail, or healthcare preferred. What We Offer: A leadership role in an innovation-driven team Exposure to end-to-end AI product development in a dynamic industry environment Opportunities to lead, innovate, and mentor Competitive salary and benefits package 6-day work culture supporting growth and accountability This is a startup environment but with good reputable company, We are looking for someone who can work Monday to Saturday and who can lead a team and generate new solutions and ideas and lead / manage the project effectively-- Please fill this given below form before applying https://forms.gle/8b3gdxzvc2JwnYfZ6 Show more Show less

Job Description: As a Senior Data and Applied Scientist, you will work with Pattern's Data Science team to curate and analyze data and apply machine learning models and statistical techniques to optimize advertising spend on ecommerce platforms. What you’ll do: Design, build, and maintain machine learning and statistical models to optimize advertising campaigns to improve search visibility and conversion rates on ecommerce platforms. Continuously optimize the quality of our machine learning models, especially for key metrics like search ranking, keyword bidding, CTR and conversion rate estimation Conduct research to integrate new data sources, innovate in feature engineering, fine-tuning algorithms, and enhance data pipelines for robust model performance. Analyze large datasets to extract actionable insights that guide advertising decisions. Work closely with teams across different regions (US and India), ensuring seamless collaboration and knowledge sharing. Dedicate 20% of time to MLOps for efficient, reliable model deployment and operations. What we’re looking for: Bachelor's or Master's in Data Science, Computer Science, Statistics, or a related field. 3-6 years of industry experience in building and deploying machine learning solutions. Strong data manipulation and programming skills in Python and SQL and hands-on experience with libraries such as Pandas, Numpy, Scikit-Learn, XGBoost. Strong problem-solving skills and an ability to analyze complex data. In depth expertise in a range of machine learning and statistical techniques such as linear and tree-based models along with understanding of model evaluation metrics. Experience with Git, AWS, Docker, and MLFlow is advantageous. Additional Pluses: Portfolio: An active Kaggle or Github profile showcasing relevant projects. Domain Knowledge: Familiarity with advertising and ecommerce concepts, which would help in tailoring models to business needs. Pattern is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Show more Show less

Job Title: Machine Learning Engineer (Gen AI and AI ML) Job Summary Primary Skills: Master's degree from Computer science, Mathematics, Statistics and other relative disciplines Experience using statistical computer languages (Python, R, etc.) to manipulate data and draw insights from large data sets. Knowledge of a variety of machine learning techniques (clustering, decision trees, boosting, artificial neural networks, etc.) and their real-world advantages/drawbacks. Knowledge of popular ML and non-ML libraries: Tensorflow, Torch, Sklearn, Scipy, Xgboost,… Knowledge of popular cloud infrastructure: Google Cloud, AWS, Microsoft Azure, … Excellent written and verbal communication skills for coordinating across teams. Experience in code management using Git Secondary Skills Experience in Ecommerce or Advertising verticals is a plus Experience on NLP, Time Series Forecasting, Computer vision, and other relative domains is a plus Experience on popular NLP and Computer vision libraries is a plus: Spacy, NLTK, OpenCV, … Experience on parallel computing and GPU acceleration is a plus Experience on LLM’s utilization and fine tuning, GEN AI solutioning along with RAG is a must Responsibilities Mine and analyze data from company databases to drive optimization and improvement for product development, marketing techniques and business strategies. Assess the effectiveness and accuracy of new data sources and data gathering techniques. Transforming data science prototypes and applying appropriate algorithms and tools. Develop custom ML and non-ML models and algorithms to apply to data sets. Define the evaluation approach of models. Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes. Coordinate with different functional teams to implement models and monitor outcomes. Keeping abreast of developments in corresponding domains. Skills: r,sklearn,nltk,torch,llm,microsoft azure,aws,scipy,git,opencv,machine learning techniques,machine learning,google cloud,xgboost,spacy,nlp,tensorflow,python,code management,gen ai,time series forecasting,computer vision Show more Show less

Job Role You’ll work at the intersection of Generative AI, Machine Learning, and personal finance. You’ll partner with Product, Engineering, and Ops to: • Drive smarter decision-making using AI. • Improve customer outcomes by personalising experiences. • Launch agentic AI systems to streamline sales, support, and credit evaluation. Key Responsibilities Design, build, and deploy machine learning and generative AI models. Translate real-world business problems into data science solutions — from ideation to production. Collaborate with engineers to set up scalable data pipelines and APIs. Constantly monitor model performance and retrain as needed. Build solutions that leverage LLMs for tasks like summarization, sentiment detection, classification, and recommendations. Stay up-to-date with developments in GenAI and actively experiment with new techniques. Required Qualifications Strong foundation in machine learning, feature engineering, and model deployment. Hands-on experience implementing GenAI use cases (e.g., prompt engineering, RAG, embeddings). Proficiency in Python, SQL, and ML libraries like scikit-learn, XGBoost, TensorFlow, or PyTorch. Familiarity with LLM frameworks (LangChain, OpenAI, HuggingFace) and cloud services (AWS/GCP/Azure). Demonstrated ability to take AI models from notebook to production. Excellent communication skills and ability to collaborate cross-functionally. Preferred Qualification Prior experience in Fintech, BFSI, or working with credit and customer data. Understanding of ethical AI, data privacy, and model fairness. Familiarity with customer-facing AI products (chatbots, agentic workflows). What We Offer • A high-impact role at a company solving a real societal problem. • The opportunity to shape AI strategy and product direction from the ground up. • A culture that values curiosity, ownership, and bold thinking. Show more Show less

About Ericsson Ericsson is a leading provider of telecommunications equipment and services to mobile and fixed network operators globally. Our innovative solutions empower individuals, businesses, and societies to explore their full potential in the Networked Society. We are seeking a highly skilled and experienced Data Scientist to join our dynamic team at Ericsson. As a Data Scientist, you will be responsible for leveraging advanced analytics and machine learning techniques to drive actionable insights and solutions for our telecom domain. This role requires a deep understanding of data science methodologies, strong programming skills, and proficiency in cloud-based environments. Key Responsibilities Develop and deploy machine learning models for various applications including chat-bot, XGBoost, random forest, NLP, computer vision, and generative AI. Utilize Python for data manipulation, analysis, and modeling tasks. Proficient in SQL for querying and analyzing large datasets. Experience with Docker and Kubernetes for containerization and orchestration of applications. Basic knowledge of PySpark for distributed computing and data processing. Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions. Deploy machine learning models into production environments and ensure scalability and reliability. Preferably have experience working with Google Cloud Platform (GCP) services for data storage, processing, and deployment. Qualifications Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's degree or PhD is preferred. 8-12 years of experience in data science and machine learning roles, preferably within the telecommunications or related industry. Proven experience in model development, evaluation, and deployment. Strong programming skills in Python and SQL. Familiarity with Docker, Kubernetes, and PySpark. Solid understanding of machine learning techniques and algorithms. Experience working with cloud platforms, preferably GCP. Excellent problem-solving skills and ability to work independently as well as part of a team. Strong communication and presentation skills, with the ability to explain complex analytical concepts to non-technical stakeholders. Why join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like. Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more. Primary country and city: India (IN) || Noida Req ID: 767292

Company Overview Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM). What You'll Do You will play an important role in applying and implementing effective machine learning solutions, with a significant focus on Generative AI. You will work with product and engineering teams to contribute to data-driven product strategies, explore and implement GenAI applications, and deliver impactful insights. This position is an individual contributor role reporting to the Senior Manager, Data Science. Responsibility Experiment with, apply, and implement DL/ML models, with a strong emphasis on Large Language Models (LLMs), Agentic Frameworks, and other Generative AI techniques to predict user behavior, enhance product features, and improve automation Utilize and adapt various GenAI techniques (e.g., prompt engineering, RAG, fine-tuning existing models) to derive actionable insights, generate content, or create novel user experiences Collaborate with product, engineering, and other teams (e.g., Sales, Marketing, Customer Success) to build Agentic system to run campaigns at-scale Conduct in-depth analysis of customer data, market trends, and user insights to inform the development and improvement of GenAI-powered solutions Partner with product teams to design, administer, and analyze the results of A/B and multivariate tests, particularly for GenAI-driven features Leverage data to develop actionable analytical insights & present findings, including the performance and potential of GenAI models, to stakeholders and team members Communicate models, frameworks (especially those related to GenAI), analysis, and insights effectively with stakeholders and business partners Stay updated on the latest advancements in Generative AI and propose their application to relevant business problems Complete assignments with a sense of urgency and purpose, identify and help resolve roadblocks, and collaborate with cross-functional team members on GenAI initiatives Job Designation Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law. What You Bring Basic Bachelor's or Master's degree in Computer Science, Physics, Mathematics, Statistics, or a related field 3+ years of hands-on experience in building data science applications and machine learning pipelines, with demonstrable experience in Generative AI projects Experience with Python for research and software development purposes, including common GenAI libraries and frameworks Strong knowledge of common machine learning, deep learning, and statistics frameworks and concepts, with a specific understanding of Large Language Models (LLMs), transformer architectures, and their applications Experience with or exposure to prompt engineering, and utilizing pre-trained LLMs (e.g., via APIs or open-source models) Experience with large datasets, distributed computing, and cloud computing platforms (e.g., AWS, Azure, GCP) Proficiency with relational databases (e.g., SQL) Experience in training, evaluating, and deploying machine learning models in production environments, with an interest in MLOps for GenAI Proven track record in contributing to ML/GenAI projects from ideation through to deployment and iteration Experience using machine learning and deep learning algorithms like CatBoost, XGBoost, LGBM, Feed Forward Networks for classification, regression, and clustering problems, and an understanding of how these can complement GenAI solutions Experience as a Data Scientist, ideally in the SaaS domain with some focus on AI-driven product features Preferred PhD in Statistics, Computer Science, or Engineering with specialization in machine learning, AI, or Statistics, with research or projects in Generative AI 5+ years of prior industry experience, with at least 1-2 years focused on GenAI applications Previous experience applying data science and GenAI techniques to customer success, product development, or user experience optimization Hands-on experience with fine-tuning LLMs or working with RAG methodologies Experience with or knowledge of experimentation platforms (like DataRobot) and other AI related ones (like CrewAI) Experience with or knowledge of the software development lifecycle/agile methodology, particularly in AI product development Experience with or knowledge of Github, JIRA/Confluence Contributions to open-source GenAI projects or a portfolio of GenAI related work Programming Languages like Python, SQL; familiarity with R Ability to break down complex technical concepts (including GenAI) into simple terms to present to diverse, technical, and non-technical audiences Life At Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live. Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. Our global benefits Paid time off Take time to unwind with earned days off, plus paid company holidays based on your region. Paid parental leave Take up to six months off with your child after birth, adoption or foster care placement. Full health benefits Options for 100% employer-paid health plans from day one of employment. Retirement plans Select retirement and pension programs with potential for employer contributions. Learning & development Grow your career with coaching, online courses and education reimbursements. Compassionate care leave Paid time off following the loss of a loved one and other life-changing events.

About Ericsson Ericsson is a leading provider of telecommunications equipment and services to mobile and fixed network operators globally. Our innovative solutions empower individuals, businesses, and societies to explore their full potential in the Networked Society. We are seeking a highly skilled and experienced Data Scientist to join our dynamic team at Ericsson. As a Data Scientist, you will be responsible for leveraging advanced analytics and machine learning techniques to drive actionable insights and solutions for our telecom domain. This role requires a deep understanding of data science methodologies, strong programming skills, and proficiency in cloud-based environments. Key Responsibilities Develop and deploy machine learning models for various applications including chat-bot, XGBoost, random forest, NLP, computer vision, and generative AI. Utilize Python for data manipulation, analysis, and modeling tasks. Proficient in SQL for querying and analyzing large datasets. Experience with Docker and Kubernetes for containerization and orchestration of applications. Basic knowledge of PySpark for distributed computing and data processing. Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions. Deploy machine learning models into production environments and ensure scalability and reliability. Preferably have experience working with Google Cloud Platform (GCP) services for data storage, processing, and deployment. Qualifications Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's degree or PhD is preferred. 8-12 years of experience in data science and machine learning roles, preferably within the telecommunications or related industry. Proven experience in model development, evaluation, and deployment. Strong programming skills in Python and SQL. Familiarity with Docker, Kubernetes, and PySpark. Solid understanding of machine learning techniques and algorithms. Experience working with cloud platforms, preferably GCP. Excellent problem-solving skills and ability to work independently as well as part of a team. Strong communication and presentation skills, with the ability to explain complex analytical concepts to non-technical stakeholders. Why join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like. Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more. Primary country and city: India (IN) || Noida Req ID: 767292

Noida,Uttar Pradesh,India +2 more Job ID 767292 About Ericsson Ericsson is a leading provider of telecommunications equipment and services to mobile and fixed network operators globally. Our innovative solutions empower individuals, businesses, and societies to explore their full potential in the Networked Society. We are seeking a highly skilled and experienced Data Scientist to join our dynamic team at Ericsson. As a Data Scientist, you will be responsible for leveraging advanced analytics and machine learning techniques to drive actionable insights and solutions for our telecom domain. This role requires a deep understanding of data science methodologies, strong programming skills, and proficiency in cloud-based environments. Key Responsibilities Develop and deploy machine learning models for various applications including chat-bot, XGBoost, random forest, NLP, computer vision, and generative AI. Utilize Python for data manipulation, analysis, and modeling tasks. Proficient in SQL for querying and analyzing large datasets. Experience with Docker and Kubernetes for containerization and orchestration of applications. Basic knowledge of PySpark for distributed computing and data processing. Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions. Deploy machine learning models into production environments and ensure scalability and reliability. Preferably have experience working with Google Cloud Platform (GCP) services for data storage, processing, and deployment. Qualifications Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's degree or PhD is preferred. 8-12 years of experience in data science and machine learning roles, preferably within the telecommunications or related industry. Proven experience in model development, evaluation, and deployment. Strong programming skills in Python and SQL. Familiarity with Docker, Kubernetes, and PySpark. Solid understanding of machine learning techniques and algorithms. Experience working with cloud platforms, preferably GCP. Excellent problem-solving skills and ability to work independently as well as part of a team. Strong communication and presentation skills, with the ability to explain complex analytical concepts to non-technical stakeholders. Why join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? find all you need to know about what our typical hiring process looks like. Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more. Primary country and city: India (IN) || Noida Req ID: 767292

Master's degree in Computer Science, Statistics, Mathematics, or a related field. 7+ years of experience in data science and machine learning with a strong focus on model development and deployment. Expert-level knowledge of statistics, including probability theory, hypothesis testing, and statistical inference. In-depth knowledge of machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, xgboost, and ensemble learning. Strong programming skills in Python and proficiency in data science libraries like pandas, scikit-learn, numpy, Pytorch/Keras, and TensorFlow. Experience with cloud computing platforms, particularly Google Cloud Platform (GCP). Excellent data visualization skills using tools like matplotlib, seaborn, or Tableau. Strong communication and presentation skills, both written and verbal. Evaluate the performance of machine learning models and refine them to improve accuracy andgeneralizability. Communicate data insights to stakeholders in a clear and concise manner, using data visualization techniques and storytelling. collaborate with data engineers, software developers, and business stakeholders to integrate data science solutions into products and services. Stay up-to-date with the latest trends and developments in data science, machine learning, and artificial intelligence. GExperience with Natural Language Processing (NLP) and Computer Vision (CV) techniques. Knowledge of DevOps methodologies and practices for continuous integration/continuous delivery (CI/CD). Experience with data warehousing and data lakes solutions like BigQuery or Snowflake. Familiarity with real-time data processing and streaming analytics. Passion for learning and staying at the forefront of data science and machine learning advancements. Show more Show less

Company Description Tatvic, is a marketing analytics company focusing on generating insights from data using long association with Google & its infrastructure. We breed,recognize and reward performance. As a company we are growing very fast & we are under transformation. To enable this transformation we need future leaders with eyesight which balances execution & strategic understanding. Website: www.tatvic.com Mission The Senior Data Scientist will provide value to clients by deriving actionable insights from data through feature definition, machine learning model creation, testing and validation, optimization, and presenting results in an actionable format. The goal is to enable data-driven business decisions for clients. Role Responsibilities Responsibilities w.r.t Customer: Communicating with Customers to discover and understand the problem statement. Design a Solution that clearly aligns the Problem Statement, Solution Details, Output, Success Criteria and how the impact of the Solution aligns to a specific Business Objective. Designing a solution. Bird’s eye view of the Platform to create an effective solution. Feature Engineering: Identify features that would matter and ensure that the logic for feature selection is transparent & explainable. Model Selection: Use Pre-trained Models, AutoML, APIs or individual algorithms & libraries for effective model selection for optimal implementation. Optimize the model to increase its effectiveness with proper data cleansing and feature engineering refinements. Deploy the model for Batch or real-time predictions using methodologies like MLOps. Display or export the output into a visualization platform. Create a POC for providing data insight for the customer at short notice. Maintaining and Managing Project execution trackers and documentation. Keep the promises made to the customer in terms of deliverables, deadlines and quality. Innovation And Asset Building Responsibilities Design and Build reusable solutions that can be reused for multiple customers. Create clear documentation on architecture and design concepts & technical decisions in the project. Conduct internal sessions to educate cross-team stakeholders to improve literacy of the domain & solutions. Maintain coding standards & build reusable code & libraries for future use and enhancing Engineering at Tatvic. Stay up-to-date with innovations in data science and its applications in Tatvic relevant domains. Frequently Perform POCs to get hands-on experience with new technologies, including Google Cloud tools designed for Data Science applications. Explore the usage of data science in various business and web analytics applications Technical Skills Data Handling: Manage data from diverse sources, including structured tables, unstructured text, images, videos, and streaming/real-time data. For scalable data processing and analysis, utilize cloud platforms (preferably Google Cloud) such as BigQuery, VertexAI, and Cloud Storage. Feature Engineering: Identify and select relevant features with transparent and explainable logic. Design new derived features to enhance model performance and enable deeper insights. Utilize advanced techniques like Pearson Coefficient and SHAP values for feature importance and correlation analysis. Model Development: Select and build models based on problem requirements, using pre-trained models, AutoML, or custom algorithms. Experience in Linear, Non-linear, Timeseries (RNNs, LSTMs), Tree-based models (XGBoost, LightGBM), and other foundational approaches. Apply advanced modeling techniques like CNNs for image processing, RCNNs, YOLO for object detection, and RAGs and LLM tuning for text and search-related tasks. Optimize models with hyperparameter tuning, Bayesian optimization, and appropriate evaluation strategies. Model Evaluation: Assess model performance using metrics suited for data type and problem type: For categorical data: Precision, Recall, F1 Score, ROC-AUC, and Precision-Recall curves. For numerical data: Metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), and R-squared (R²). Deployment & MLOps: Deploy models for batch or real-time predictions using MLOps frameworks, leveraging tools such as VertexAI and Kubeflow for efficient and scalable deployment pipelines. Integrate outputs with visualization platforms to deliver actionable insights and drive decision-making. Innovation: Stay current with trends in AI and data science, including LLMs, grounding techniques, and innovations in temporal and sequential data modeling. Regularly conduct POCs to experiment with emerging tools and technologies. Code Practices & Engineering: Write clean, maintainable, and scalable code following industry best practices. Adhere to version control (e.g., Git) for collaborative development and maintain coding standards. Implement error handling, logging, and monitoring to ensure reliability in production systems. Collaborate with other teams to integrate data science models into broader system architectures. Performance Optimization: Optimize model and data processing pipelines for computational efficiency and scalability. Use parallel processing, distributed computing, and hardware accelerators (e.g., GPUs, TPUs) where applicable. Documentation & Reusability: Maintain comprehensive technical documentation for all solutions. Design and build reusable assets to streamline future implementations. Technical Tools And Platforms Google Cloud (BigQuery, VertexAI, Cloud Storage) Python (Libraries: TensorFlow, Scikit-learn, XGBoost, LightGBM, etc.) SQL/NoSQL databases MLOps frameworks (Kubeflow, Vertex AI Pipelines) Visualization tools (Power BI, Tableau, Google Data Studio) Show more Show less