AI/ML Models: Experience with Automated Valuation Models (AVM) and real-world deployment of machine learning models LangChain: Proficient in using LangChain for building LLM-powered applications Data Science Toolkit: Hands-on with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter Feature Engineering: Strong background in feature engineering and data intelligence extraction Data Handling: Comfortable with structured, semi-structured, and unstructured data Production Integration: Experience integrating models into APIs and production environments using Python-based frameworks Location-Remote, Delhi NCR, Bangalore, Chennai, Pune, Kolkata, Ahmedabad, Mumbai, Hyderabad

Verint is a leader in CX automation. The world’s most iconic brands rely on our open platform and team of AI-powered bots to create tangible AI business outcomes, now. Today’s organizations face skyrocketing expectations for delivering better customer experiences (CX) across every channel. But hiring more workers and increasing workforce expenses isn’t a sustainable option. AI offers a solution – and that’s where Verint comes in. We empower brands with groundbreaking AI via our next-gen open platform. The first of its kind, Verint Open Platform helps organizations increase CX automation to achieve their strategic objectives and realize significant ROI. The Verint Open Platform provides a team of AI-powered bots to augment human staff across the enterprise, including contact centers, web/mobile channels, branches, back offices, CX offices, and more. The result? Organizations can create capacity, lower costs, and continually improve CX. It’s all possible with CX automation. Center of Excellence in Financial Analytics and Innovation Team Description: The Center of Excellence in Financial Analytics comprises a dynamic and diverse team of professionals with expertise in finance, computer science, data analytics, data engineering, machine learning & AI, and FP&A. Our team is dedicated to driving innovation, leveraging analytics techniques, and utilizing financial insights to optimize decision-making within the organization. Collaborative and driven, our team members work closely together to deliver high-quality business insights and contribute to the success of the CFO Organization. Location: Bangalore India Job Title: Specialist – Machine Learning and AI Job Summary: We are seeking a highly skilled and motivated Specialist – Machine Learning and AI to join our Financial Analytics Center of Excellence in Bangalore. This role will support cross-functional business teams—including CFO, CTO/Product Analytics, Investor Relations, Corporate FP&A, Customer Success, Sales Operations, Reporting & Consolidation, and Partner Alliances—by designing and deploying advanced AI/ML solutions to solve complex business challenges. The ideal candidate will bring 5–10 years of hands-on experience in applying machine learning and artificial intelligence in financial analytics, product usage analysis, and general business operations in a B2B software/SaaS environment. Job Description: Collaborate with finance, product, and operations stakeholders to understand business questions and deliver ML/AI-driven solutions that improve forecasting, anomaly detection, and process automation. Lead the design and deployment of machine learning models including supervised, unsupervised, and reinforcement learning approaches to solve use cases in revenue forecasting, GL mapping, customer expansion recommendations, customer retention, churn and product analytics. Own the end-to-end ML pipeline: data preparation, feature engineering, model selection, training, validation, and deployment. Design and implement MLOps and automation pipelines to streamline the deployment and monitoring of ML models. Enhance and maintain existing models for intelligent classification to support business growth. Partner with data engineers and analysts to ensure data quality, scalable architecture, and secure model integration with business applications and dashboards. Provide thought leadership in identifying emerging AI/ML trends and proactively propose innovative use cases to improve business outcomes. Generate explainable, transparent outputs that can be translated into business actions and executive-level insights. Required Qualifications: Bachelor's or master’s degree in computer science, Data Science, Applied Mathematics, Statistics, or a related field. 5–10 years of hands-on experience applying machine learning and AI in a business context, preferably in the software/SaaS industry. Strong programming skills in Python and libraries like scikit-learn, XGBoost, TensorFlow, PyTorch, etc. Demonstrated success with ML use cases such as: GL classification, predictive modeling for financial KPIs, customer behavior prediction, or recommendation systems. Experience working in cross-functional teams with finance, sales, customer success, and product functions. Proficiency in SQL and working with structured/unstructured datasets. Ability to communicate complex technical solutions clearly to non-technical stakeholders and executive audiences. Preferred Qualifications: Prior experience supporting FP&A, Product Analytics, or GTM Strategy functions. Exposure to cloud-based ML deployment (Azure, AWS, or GCP). Basic knowledge of Large Language Models (LLMs) and Generative AI tools. Familiarity with BI tools such as Power BI or Tableau for integrating ML output into business reporting. Why Join Us? Be part of a growing, high-impact Financial Analytics Center of Excellence. Work on meaningful business challenges using cutting-edge AI/ML approaches. Collaborate with senior business leaders across Business Finance, Product, and Customer organizations. Drive innovation and help shape the next phase of our AI/ML capability in a dynamic business team in global software company. Show more Show less

About Company: Hansa Cequity is one of the leading data-driven, connected CX services providers in India. We offer integrated solutions in Marketing, Data Analytics, MarTech, Campaign Management, Digital, and Contact Center services to help clients acquire, engage, and retain customers profitably. By combining data-driven marketing strategy with cutting-edge technology, we empower brands to understand their customers deeply and build meaningful, multichannel engagement. Our goal is to deliver measurable business growth using best-in-class tools, platforms, and insights. We are recognized among the Top 50 Analytics Companies in India . To learn more, visit: www.hansacequity.com LinkedIn: Hansa Cequity Job Description: We are seeking a Data Scientist with strong expertise in classical machine learning and experience working with automotive datasets . The role focuses on building predictive models, marketing mix analysis, and end-to-end data pipeline development. Experience in marketing analytics is essential. Exposure to Generative AI is a plus, not a requirement. Key Responsibilities: Develop and deploy ML models (classification, regression, forecasting) for business use cases in the automotive domain. Analyze customer and vehicle data to derive insights for campaigns, retention, and personalization. Implement NLP techniques and marketing mix models for campaign optimization. Build data pipelines to ensure clean and real-time data processing. Collaborate with cross-functional teams to translate insights into strategy. Qualifications: Bachelor’s/Master’s in Computer Science, Data Science, or related field. 3–5 years of experience in ML and marketing analytics. Strong in Python, R, SQL, Azure; experience with scikit-learn, XGBoost, TensorFlow, or PyTorch. Automotive domain experience is mandatory. Familiarity with GenAI tools (OpenAI, LangChain, etc.) is a bonus or a plus point. Show more Show less

Data Scientist @ DevOn The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. You have at least 3 years of experience as a Data Scientist. Responsibilities 🚀 We are Hiring – Data Scientists (Immediate to 15 Days Joiners) Are you passionate about building models that make a real-world impact? At DevOn , you’ll: 🔹 Work on real-world classification & regression problems 🔹 Build models using XGBoost, LightGBM, Random Forest 🔹 Perform feature engineering , scaling , handle missing data & outliers 🔹 Apply model evaluation techniques – PR Curve, F1, Cross-validation, R² 🔹 Tune hyperparameters and deploy using CI/CD, Docker, AWS 🔹 Collaborate with engineers and analysts to take models to production 🔹 Document experiments, build reproducible pipelines, and present insights Your profile: ✔ Strong hands-on experience in ML model development end-to-end ✔ Solid understanding of model performance metrics and tuning ✔ Expertise in Python, SQL, GitHub, Jupyter, TensorFlow/PyTorch ✔ Knowledge of bias-variance tradeoff , bagging vs boosting , IQR, etc. ✔ Experience with cloud platforms (AWS preferred) ✔ Bonus: A/B testing or causal inference exposure 📩 Send your resume to: TA-IN-Consulting@devon.nl 📅 Looking for immediate joiners or max 15 days Qualifications Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) At least 3-years of experience in quantitative analytics or data modeling Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms Fluency in a programming language (Python & SQL) Show more Show less

About Zupee We are the biggest online gaming company with largest market share in the Indian gaming sector’s largest segment — Casual & Boardgame. We make skill-based games that spark joy in the everyday lives of people by engaging, entertaining, and enabling earning while at play. In the three plus years of existence, Zupee has been on a mission to improve people’s lives by boosting their learning ability, skills, and cognitive aptitude through scientifically designed gaming experiences. Zupee presents a timeout from the stressful environments we live in today and sparks joy in the lives of people through its games. Zupee invests in people and bets on creating excellent user experiences to drive phenomenal growth. We have been running profitable at EBT level since Q3, 2020 while closing Series B funding at $102 million, at a valuation of $600 million. Zupee is all set to transform from a fast-growing startup to a firm contender for the biggest gaming studio in India.. ABOUT THE JOB Role: Lead Machine Learning Engineer Reports to: Manager- Data Scientist Location: Gurgaon Job Summary: We seek a an individual to drive innovation in AI ML-based algorithms and personalized offer experiences. This role will focus on designing and implementing advanced machine learning models, including reinforcement learning techniques like Contextual Bandits, Q-learning, SARSA, and more. By leveraging algorithmic expertise in classical ML and statistical methods, you will develop solutions that optimize pricing strategies, improve customer value, and drive measurable business impact. Qualifications: - 6+ years in machine learning, 4+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence. - Expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization. - Proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding. - Proficient in Python and SQL (including Window Functions, Group By, Joins, and Partitioning). - Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch - Knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing. Key Responsibilities Algorithm Development: Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations. Reinforcement Learning Expertise: Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization, to solve pricing and optimization challenges. AI Agents for Pricing: Build AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion. Rapid ML Prototyping: Experience in quickly building, testing, and iterating on ML prototypes to validate ideas and refine algorithms. Feature Engineering: Engineer large-scale consumer behavioral feature stores to support ML models, ensuring scalability and performance. Cross-Functional Collaboration: Work closely with Marketing, Product, and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact. Controlled Experiments: Design, analyze, and troubleshoot A/B and multivariate tests to validate the effectiveness of your models. Required Skills and Experience UPLIFT MODELING BAYESIAN OPTIMIZATION MULTI-ARMED BANDITS CONTEXTUAL BANDITS PRICING OPTIMIZATION REINFORCEMENT LEARNING Show more Show less

About Zupee We are the biggest online gaming company with largest market share in the Indian gaming sector’s largest segment — Casual & Boardgame. We make skill-based games that spark joy in the everyday lives of people by engaging, entertaining, and enabling earning while at play. In the three plus years of existence, Zupee has been on a mission to improve people’s lives by boosting their learning ability, skills, and cognitive aptitude through scientifically designed gaming experiences. Zupee presents a timeout from the stressful environments we live in today and sparks joy in the lives of people through its games. Zupee invests in people and bets on creating excellent user experiences to drive phenomenal growth. We have been running profitable at EBT level since Q3, 2020 while closing Series B funding at $102 million, at a valuation of $600 million. Zupee is all set to transform from a fast-growing startup to a firm contender for the biggest gaming studio in India.. ABOUT THE JOB Role: Senior Machine Learning Engineer Reports to: Manager- Data Scientist Location: Gurgaon Job Summary: We seek a an individual to drive innovation in AI ML-based algorithms and personalized offer experiences. This role will focus on designing and implementing advanced machine learning models, including reinforcement learning techniques like Contextual Bandits, Q-learning, SARSA, and more. By leveraging algorithmic expertise in classical ML and statistical methods, you will develop solutions that optimize pricing strategies, improve customer value, and drive measurable business impact. Qualifications: - 3+ years in machine learning, 2+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence. - Expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization. - Proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding. - Proficient in Python and SQL (including Window Functions, Group By, Joins, and Partitioning). - Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch - Knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing. Key Responsibilities - Algorithm Development: Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations. - Reinforcement Learning Expertise: Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization, to solve pricing and optimization challenges. -AI Agents for Pricing: Build AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion. - Rapid ML Prototyping: Experience in quickly building, testing, and iterating on ML prototypes to validate ideas and refine algorithms. -Feature Engineering: Engineer large-scale consumer behavioral feature stores to support ML models, ensuring scalability and performance. -Cross-Functional Collaboration: Work closely with Marketing, Product, and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact. -Controlled Experiments: Design, analyze, and troubleshoot A/B and multivariate tests to validate the effectiveness of your models. Required Skills and Experience UPLIFT MODELING BAYESIAN OPTIMIZATION MULTI-ARMED BANDITS CONTEXTUAL BANDITS PRICING OPTIMIZATION REINFORCEMENT LEARNING Show more Show less

Job Title : Senior Data Scientist (SDS 2) Experience: 4+ years Location : Bengaluru (Hybrid) Company Overview: Akaike Technologies is a dynamic and innovative AI-driven company dedicated to building impactful solutions across various domains . Our mission is to empower businesses by harnessing the power of data and AI to drive growth, efficiency, and value. We foster a culture of collaboration , creativity, and continuous learning , where every team member is encouraged to take initiative and contribute to groundbreaking projects. We value diversity, integrity, and a strong commitment to excellence in all our endeavors. Job Description: We are seeking an experienced and highly skilled Senior Data Scientist to join our team in Bengaluru. This role focuses on driving innovative solutions using cutting-edge Classical Machine Learning, Deep Learning, and Generative AI . The ideal candidate will possess a blend of deep technical expertise , strong business acumen, effective communication skills , and a sense of ownership . During the interview, we look for a proven track record in designing, developing, and deploying scalable ML/DL solutions in a fast-paced, collaborative environment. Key Responsibilities: ML/DL Solution Development & Deployment: Design, implement, and deploy end-to-end ML/DL, GenAI solutions, writing modular, scalable, and production-ready code. Develop and implement scalable deployment pipelines using Docker and AWS services (ECR, Lambda, Step Functions). Design and implement custom models and loss functions to address data nuances and specific labeling challenges. Ability to model in different marketing scenarios of a product life cycle ( Targeting, Segmenting, Messaging, Content Recommendation, Budget optimisation, Customer scoring, risk and churn ), and data limitations(Sparse or incomplete labels, Single class learning) Large-Scale Data Handling & Processing: Efficiently handle and model billions of data points using multi-cluster data processing frameworks (e.g., Spark SQL, PySpark ). Generative AI & Large Language Models (LLMs): Leverage in-depth understanding of transformer architectures and the principles of Large and Small Language Models . Practical experience in building LLM-ready Data Management layers for large-scale structured and unstructured data . Apply foundational understanding of LLM Agents, multi-agent systems (e.g., Agent-Critique, ReACT, Agent Collaboration), advanced prompting techniques, LLM eval uation methodologies, confidence grading, and Human-in-the-Loop systems. Experimentation, Analysis & System Design: Design and conduct experiments to test hypotheses and perform Exploratory Data Analysis (EDA) aligned with business requirements. Apply system design concepts and engineering principles to create low-latency solutions capable of serving simultaneous users in real-time. Collaboration, Communication & Mentorship: Create clear solution outlines and e ffectively communicate complex technical concepts to stakeholders and team members. Mentor junior team members, providing guidance and bridging the gap between business problems and data science solutions. Work closely with cross-functional teams and clients to deliver impactful solutions. Prototyping & Impact Measurement: Comfortable with rapid prototyping and meeting high productivity expectations in a fast-paced development environment. Set up measurement pipelines to study the impact of solutions in different market scenarios. Must-Have Skills: Core Machine Learning & Deep Learning: In-depth knowledge of Artificial Neural Networks (ANN), 1D, 2D, and 3D Convolutional Neural Networks (ConvNets), LSTMs , and Transformer models. Expertise in modeling techniques such as promo mix modeling (MMM) , PU Learning , Customer Lifetime Value (CLV) , multi-dimensional time series modeling, and demand forecasting in supply chain and simulation. Strong proficiency in PU learning, single-class learning, representation learning, alongside traditional machine learning approaches. Advanced understanding and application of model explainability techniques. Data Analysis & Processing: Proficiency in Python and its data science ecosystem, including libraries like NumPy, Pandas, Dask, and PySpark for large-scale data processing and analysis. Ability to perform effective feature engineering by understanding business objectives. ML/DL Frameworks & Tools: Hands-on experience with ML/DL libraries such as Scikit-learn, TensorFlow/Keras, and PyTorch for developing and deploying models. Natural Language Processing (NLP): Expertise in traditional and advanced NLP techniques, including Transformers (BERT, T5, GPT), Word2Vec, Named Entity Recognition (NER), topic modeling, and contrastive learning. Cloud & MLOps: Experience with the AWS ML stack or equivalent cloud platforms. Proficiency in developing scalable deployment pipelines using Docker and AWS services (ECR, Lambda, Step Functions). Problem Solving & Research: Strong logical and reasoning skills. Good understanding of the Python Ecosystem and experience implementing research papers. Collaboration & Prototyping: Ability to thrive in a fast-paced development and rapid prototyping environment. Relevant to Have: Expertise in Claims data and a background in the pharmaceutical industry . Awareness of best software design practices . Understanding of backend frameworks like Flask. Knowledge of Recommender Systems, Representative learning, PU learning. Benefits and Perks: Competitive ESOP grants. Opportunity to work with Fortune 500 companies and world-class teams. Support for publishing papers and attending academic/industry conferences. Access to networking events, conferences, and seminars. Visibility across all functions at Akaike, including sales, pre-sales, lead generation, marketing, and hiring. Appendix Technical Skills (Must Haves) Having deep understanding of the following Data Processing : Wrangling : Some understanding of querying database (MySQL, PostgresDB etc), very fluent in the usage of the following libraries Pandas, Numpy, Statsmodels etc. Visualization : Exposure towards Matplotlib, Plotly, Altair etc. Machine Learning Exposure : Machine Learning Fundamentals, For ex: PCA, Correlations, Statistical Tests etc. Time Series Models, For ex: ARIMA, Prophet etc. Tree Based Models, For ex: Random Forest, XGBoost etc.. Deep Learning Models, For ex: Understanding and Experience of ConvNets, ResNets, UNets etc. GenAI Based Models : Experience utilizing large-scale language models such as GPT-4 or other open-source alternatives (such as Mistral, Llama, Claude) through prompt engineering and custom finetuning. Code Versioning Systems : Github, Git If you're interested in the job opening, please apply through the Keka link provided here: https://akaike.keka.com/careers/jobdetails/26215 Show more Show less

JOB DESCRIPTION • Strong in Python with libraries such as polars, pandas, numpy, scikit-learn, matplotlib, tensorflow, torch, transformers • Must have: Deep understanding of modern recommendation systems including two-tower , multi-tower , and cross-encoder architectures • Must have: Hands-on experience with deep learning for recommender systems using TensorFlow , Keras , or PyTorch • Must have: Experience generating and using text and image embeddings (e.g., CLIP , ViT , BERT , Sentence Transformers ) for content-based recommendations • Must have: Experience with semantic similarity search and vector retrieval for matching user-item representations • Must have: Proficiency in building embedding-based retrieval models , ANN search , and re-ranking strategies • Must have: Strong understanding of user modeling , item representations , temporal/contextual personalization • Must have: Experience with Vertex AI for training, tuning, deployment, and pipeline orchestration • Must have: Experience designing and deploying machine learning pipelines on Kubernetes (e.g., using Kubeflow Pipelines , Kubeflow on GKE , or custom Kubernetes orchestration ) • Should have experience with Vertex AI Matching Engine or deploying Qdrant , F AISS , ScaNN , on GCP for large-scale retrieval • Should have experience working with Dataproc (Spark/PySpark) for feature extraction, large-scale data prep, and batch scoring • Should have a strong grasp of cold-start problem solving using metadata and multi-modal embeddings • Good to have: Familiarity with Multi-Modal Retrieval Models combining text, image, and tabular features • Good to have: Experience building ranking models (e.g., XGBoost , LightGBM , DLRM ) for candidate re-ranking • Must have: Knowledge of recommender metrics (Recall@K, nDCG, HitRate, MAP) and offline evaluation frameworks • Must have: Experience running A/B tests and interpreting results for model impact • Should be familiar with real-time inference using Vertex AI , Cloud Run , or TF Serving • Should understand feature store concepts , embedding versioning , and serving pipelines • Good to have: Experience with streaming ingestion (Pub/Sub, Dataflow) for updating models or embeddings in near real-time • Good to have: Exposure to LLM-powered ranking or personalization , or hybrid recommender setups • Must follow MLOps practices — version control, CI/CD, monitoring, and infrastructure automation GCP Tools Experience: ML & AI : Vertex AI, Vertex Pipelines, Vertex AI Matching Engine, Kubeflow on GKE, AI Platform Embedding & Retrieval : Matching Engine, FAISS, ScaNN, Qdrant, GKE-hosted vector DBs (Milvus) Storage : BigQuery, Cloud Storage, Firestore Processing : Dataproc (PySpark), Dataflow (batch & stream) Ingestion : Pub/Sub, Cloud Functions, Cloud Run Serving : Vertex AI Online Prediction, TF Serving, Kubernetes-based custom APIs, Cloud Run CI/CD & IaC : GitHub Actions, GitLab CI Show more Show less

JOB DESCRIPTION • Strong in Python and experience with Jupyter notebooks , Python packages like polars, pandas, numpy, scikit-learn, matplotlib, etc. • Must have: Experience with machine learning lifecycle , including data preparation , training , evaluation , and deployment • Must have: Hands-on experience with GCP services for ML & data science • Must have: Experience with Vector Search and Hybrid Search techniques • Must have: Experience with embeddings generation using models like BERT , Sentence Transformers , or custom models • Must have: Experience in embedding indexing and retrieval (e.g., Elastic, FAISS, ScaNN, Annoy) • Must have: Experience with LLMs and use cases like RAG (Retrieval-Augmented Generation) • Must have: Understanding of semantic vs lexical search paradigms • Must have: Experience with Learning to Rank (LTR) techniques and libraries (e.g., XGBoost, LightGBM with LTR support) • Should be proficient in SQL and BigQuery for analytics and feature generation • Should have experience with Dataproc clusters for distributed data processing using Apache Spark or PySpark • Should have experience deploying models and services using Vertex AI , Cloud Run , or Cloud Functions • Should be comfortable working with BM25 ranking (via Elasticsearch or OpenSearch ) and blending with vector-based approaches • Good to have: Familiarity with Vertex AI Matching Engine for scalable vector retrieval • Good to have: Familiarity with TensorFlow Hub , Hugging Face , or other model repositories • Good to have: Experience with prompt engineering , context windowing , and embedding optimization for LLM-based systems • Should understand how to build end-to-end ML pipelines for search and ranking applications • Must have: Awareness of evaluation metrics for search relevance (e.g., precision@k , recall , nDCG , MRR ) • Should have exposure to CI/CD pipelines and model versioning practices GCP Tools Experience: ML & AI : Vertex AI, Vertex AI Matching Engine, AutoML, AI Platform Storage : BigQuery, Cloud Storage, Firestore Ingestion : Pub/Sub, Cloud Functions, Cloud Run Search : Vector Databases (e.g., Matching Engine, Qdrant on GKE), Elasticsearch/OpenSearch Compute : Cloud Run, Cloud Functions, Vertex Pipelines , Cloud Dataproc (Spark/PySpark) CI/CD & IaC : GitLab/GitHub Actions Show more Show less

About the Role We’re looking for an experienced AI Developer with hands-on expertise in Large Language Models (LLMs) , Azure AI services , and end-to-end ML pipeline deployment . If you’re passionate about building scalable AI solutions, integrating document intelligence, and deploying models in production using Azure, this role is for you. 💡 Key Responsibilities Design and develop AI applications leveraging LLMs (e.g., GPT, BERT) for tasks like summarization, classification, and document understanding Implement solutions using Azure Document Intelligence to extract structured data from forms, invoices, and contracts Train, evaluate, and tune ML models using Scikit-learn, XGBoost , or PyTorch Build ML pipelines and workflows using Azure ML , MLflow , and integrate with CI/CD tools Deploy models to production using Azure ML endpoints , containers, or Azure Functions for real-time AI workflows Write clean, efficient, and scalable code in Python and manage code versioning using Git Work with structured and unstructured data from SQL/NoSQL databases and Data Lakes Ensure performance monitoring and logging for deployed models ✅ Skills & Experience Required Proven experience with LLMs and Prompt Engineering (e.g., GPT, BERT) Hands-on with Azure Document Intelligence for OCR and data extraction Solid background in ML model development, evaluation , and hyperparameter tuning Proficient in Azure ML Studio , model registry , and automated ML workflows Familiar with MLOps tools such as Azure ML pipelines, MLflow , and CI/CD practices Experience with Azure Functions for building serverless, event-driven AI apps Strong coding skills in Python ; familiarity with libraries like NumPy, Pandas, Scikit-learn, Matplotlib Working knowledge of SQL/NoSQL databases and Data Lakes Proficiency with Azure DevOps , Git version control, and testing frameworks Show more Show less

Line of Service Advisory Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager Job Description & Summary A career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organisational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organisations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge. Creating business intelligence from data requires an understanding of the business, the data, and the technology used to store and analyse that data. Using our Rapid Business Intelligence Solutions, data visualisation and integrated reporting dashboards, we can deliver agile, highly interactive reporting and analytics that help our clients to more effectively run their business and understand what business questions can be answered and how to unlock the answers. Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us. At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. " Job Description & Summary : Manager Advanced Analytics & ML – Financial Services Responsibilities This is for an opening at the Manager level in the Data and Analytics division at PwC India. The role will be centered around Financial services domain. A successful candidate is expected to work pro-actively and effectively on multiple client engagements and take ownership of the entire project delivery. This includes having project management skills, technical and/or functional expertise, and commitment to comply with the PwC delivery quality expectation. Further, this role requires a candidate with strong interpersonal skills, who not only enjoys the challenge of working with other teams but externally with a variety of clients as well. Strong personal and professional presence and self-confidence, capable of working effectively with senior team as well as all other levels. The candidate will be required to showcase excellent communication skills and will have demonstrated consistently the skill and capability in delivering impactful and insightful projects in the past. He/she will also be required to participate in client meetings, understand the business needs and then design end to end machine learning and analytics solutions to fulfill those business needs. They will also be expected to contribute to practice or Firm development. This may be adjudged in various ways such as serving as a mentor to other team members, by leading training/development initiatives, contributing to thought leadership papers, and developing reusable assets. Detailed role and responsibilities are provided below: Roles and Responsibilities: Develop, Review and implement Solutions applying advanced analytics techniques including but not restricted to Machine Learning, Deep Learning, AI, NLP and Visualization Troubleshoot, isolate and remediate model errors. Work on and manage large to mid-size projects, and ensure smooth service delivery on assigned products, engagements and/or geographies. Work with project leaders to analyse resource needs and gaps and devise alternative ways forward. Provide expert reviews for all projects within the assigned subject Ability to lead business development initiatives including responding to RFPs, preparation and delivery of client presentations with the objective of sales and business development. Ability to manage cross functional teams and mentor junior team members Understanding of statistical methods to enable appropriate interpretation of results Experience analyzing programs through the lens of client requirements and design optimal solutions for fulfilling those requirements. Conceptual thinking and ability to find innovative ways to solve analytical problems Should have worked on multiple analytics consulting and implementation projects Skills & Qualifications Required: Minimum 8-10 years of experience in advanced analytics and machine learning space with at least 4-6 years of experience in financial services. Advanced understanding and hands-on experience in SQL and at least one of R or Python. Basic exposure to other statistical packages such as R, Python and SAS. Deep understanding of predictive algorithms such as logistic regression, linear regression, decision trees, random forest, xgboost, SVM etc. and clustering algorithms such as k-means, k nearest neighbour, hierarchical etc. Hands on experience in NLP and deep understanding of text analytics algorithms and modelling workflow Good knowledge of neural networks, deep learning, RNN, CNN, LSTM etc, Working knowledge of big data environment setups such as Spark, PySpark, Hive, Hadoop etc. Advanced understanding of Cloud (AWS, Azure, etc.) and good exposure to Azure/AWS machine learning workbench and MLOps workflow. Excellent verbal and written communication skills. Experienced in creating power point presentations, dashboards, solving complicated client problems and communicating the precise insights. Strong organizational skills & the ability to prioritize and work on projects with great efficiency & attention to the details. Minimum qualification – B.E; MBA is preferred (with at least 5 years of exp. post MBA) Mandatory Skill Sets Advanced Analytics & ML Preferred Skill Sets Advanced Analytics & ML Years Of Experience Required 9+ Education Qualification BTech/MBA/MCA Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Bachelor of Engineering, Master of Business Administration Degrees/Field Of Study Preferred Certifications (if blank, certifications not specified) Required Skills Advanced Analytics Optional Skills Desired Languages (If blank, desired languages not specified) Travel Requirements Not Specified Available for Work Visa Sponsorship? No Government Clearance Required? No Job Posting End Date Show more Show less

We’re seeking a skilled Data Scientist with expertise in SQL, Python, AWS SageMaker , and Commercial Analytics to contribute to Team. You’ll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions. This role is ideal for a problem-solver passionate about turning data into strategic value. Key Responsibilities Model Development: Build, validate, and deploy machine learning models (e.g., recommendation engines, propensity models) using Python and AWS SageMaker to drive next-best-action decisions. Data Pipeline Design: Develop efficient SQL queries and ETL pipelines to process large-scale commercial datasets (e.g., customer behavior, transactional data). Commercial Analytics: Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities. Cross-functional Collaboration: Partner with marketing, sales, and product teams to align models with business objectives and operational workflows. Cloud Integration: Optimize model deployment on AWS, ensuring scalability, monitoring, and performance tuning. Insight Communication: Translate technical outcomes into actionable recommendations for non-technical stakeholders through visualizations and presentations. Continuous Improvement: Stay updated on advancements in AI/ML, cloud technologies, and commercial analytics trends. Qualifications Education: Bachelor’s/Master’s in Data Science, Computer Science, Statistics, or a related field. Experience: 3-4 years in data science, with a focus on commercial/customer analytics (e.g., pharma, retail, healthcare, e-commerce, or B2B sectors). Technical Skills: Proficiency in SQL (complex queries, optimization) and Python (Pandas, NumPy, Scikit-learn). Hands-on experience with AWS SageMaker (model training, deployment) and cloud services (S3, Lambda, EC2). Familiarity with ML frameworks (XGBoost, TensorFlow/PyTorch) and A/B testing methodologies. Analytical Mindset: Strong problem-solving skills with the ability to derive insights from ambiguous data. Communication: Ability to articulate technical concepts to business stakeholders. Preferred Qualifications AWS Certified Machine Learning Specialty or similar certifications. Experience with big data tools (Spark, Redshift) or ML Ops practices. Knowledge of NLP, reinforcement learning, or real-time recommendation systems. Exposure to BI tools (Tableau, Power BI) for dashboarding. Show more Show less

Job Title: AI/ML Developer (5 Years Experience) Location : Remote Job Type : Full-time Experience:5 Year Job Summary: We are looking for an experienced AI/ML Developer with at least 5 years of hands-on experience in designing, developing, and deploying machine learning models and AI-driven solutions. The ideal candidate should have strong knowledge of machine learning algorithms, data preprocessing, model evaluation, and experience with production-level ML pipelines. Key Responsibilities Model Development : Design, develop, train, and optimize machine learning and deep learning models for classification, regression, clustering, recommendation, NLP, or computer vision tasks. Data Engineering : Work with data scientists and engineers to preprocess, clean, and transform structured and unstructured datasets. ML Pipelines : Build and maintain scalable ML pipelines using tools such as MLflow, Kubeflow, Airflow, or SageMaker. Deployment : Deploy ML models into production using REST APIs, containers (Docker), or cloud services (AWS/GCP/Azure). Monitoring and Maintenance : Monitor model performance and implement retraining pipelines or drift detection techniques. Collaboration : Work cross-functionally with data scientists, software engineers, and product managers to integrate AI capabilities into applications. Research and Innovation : Stay current with the latest advancements in AI/ML and recommend new techniques or tools where applicable. Required Skills & Qualifications Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Minimum 5 years of experience in AI/ML development. Proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM. Strong understanding of statistics, data structures, and ML/DL algorithms. Experience with cloud platforms (AWS/GCP/Azure) and deploying ML models in production. Experience with CI/CD tools and containerization (Docker, Kubernetes). Familiarity with SQL and NoSQL databases. Excellent problem-solving and communication skills. Preferred Qualifications Experience with NLP frameworks (e.g., Hugging Face Transformers, spaCy, NLTK). Knowledge of MLOps best practices and tools. Experience with version control systems like Git. Familiarity with big data technologies (Spark, Hadoop). Contributions to open-source AI/ML projects or publications in relevant fields. Show more Show less

Job Description Job summary: Company Chase & Co. (NYSE: JPM) is a leading global financial services firm with operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, Company Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its Company and Chase brands. Information about Company Chase & Co. is available at Company website. Chase Consumer & Community Banking (CCB) serves consumers and small businesses with a broad range of financial services. CCB Risk Management partners with each CCB sub-line of business to identify, assess, prioritize, and remediate risk. We are currently seeking an Applied ML AI Executive Director as the Head of Credit Card Collections Risk Modeling team . In this critical role you will be managing a team of applied machine learning modelers in multiple working locations who are responsible for developing and maintaining best-in-class credit risk models catering to the collections and recovery functions within Chase Card Services. You will be responsible for identifying business opportunities for applying suitable machine learning algorithms to develop ML models that enhance the effectiveness of credit loss control. Your expertise and thought leadership in big data platforms (Hadoop/Cloud) and advanced ML techniques, such as deep learning, reinforcement learning and graph ML will substantially influence the direction of the next generation of risk models. In this highly visible role, the successful candidate will demonstrate analytic leadership through business acumen, collaboration, and effective communication skills with senior management. Success in this role requires a strong foundation in machine learning and artificial intelligence, along with deep understanding of credit risk management. The candidate should have a proven ability to manage end-end ML/AI solutions, especially deploy ML models harnessing vast amounts of data and computation into distributed systems. Job Responsibilities Collaborate with risk strategy teams and operations to understand business needs, data generating process, system capability, and potential model impact. Design machine learning solutions to address business needs, including explainable machine learning models and reinforcement learning models Manage multiple model development projects Collaborate with various partners in Marketing, Finance, Technology, Model Governance, Compliance, Risk, Legal, etc. throughout the entire modeling lifecycle. Manage model risk and related governance and controls Synthesize the findings at various points through the model development process to share actionable insights with senior leadership and other stakeholders Drive constant innovations to drive sustained improvement in collections and recovery capabilities of the firm Required Qualifications, Capabilities, And Skills Ph.D. or MS degree in Mathematics, Statistics, Computer Science, Operational Research, Econometrics, Physics, or other related quantitative fields Minimal 10-year of experience in developing and managing ML or predictive risk models in financial institutions Hand-on experience in developing and deploying real-time transaction models with massive data from various sources, internally and externally Developed ML/AI models in big data platform (Hadoop and Cloud) and deployed them into real-time scoring engines, such as mainframe, cloud or distributed computing systems Experience in developing and deploying commercial applications for machine learning that are interpretable Experience in open source programming languages for large scale data analysis such as Python / Scala / Java / PySpark Experience with supervised and unsupervised machine learning algorithms such as XGBoost, CNN, RNN, SVM, Reinforcement Learning, Markov Process Minimal 3-year experience managing a sizable team of data scientists/ modelers/ machine learning engineers Experience in managing a team in a dynamic environment of high mobility Polished and clear communications with senior management Proven leadership in client/stakeholder/partner relationship management and high-performance team development ABOUT US JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. About The Team Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes. Show more Show less

Company Description Winbold is a startup working on a AI driven investment platform. Job Description This requires immediate joining. Do not apply if you have notice period. We are looking for a very motivated Data Science and Machine Learning Engineer with hands-on experience in feature engineering, model training and building models that run and provide real-time predictions and forecasts. The candidate must be well versed with the following: ML Packages - sklean, xgboost, timeseries models Feature engineering and feature stores MLFlow for Model experimentation Model visualization Model deployment in Cloud and in hosted environments at scale For immediate consideration you can send email to me directly with a note on why you think you are the better suited for this role at madhu at winbolddatasystems.com Qualifications We dont care what degree you have or if you have one. We want a hands-on developer who can get shit done. Additional Information All your information will be kept confidential according to EEO guidelines. Show more Show less

Job Description This is a remote position. AI Engineer Duration: 6 months Location: Remote Timings: Full Time (As per company timings) Notice Period: (Immediate Joiner - Only) Experience: 3-5 Years Jd AI/ML Models: Experience with Automated Valuation Models (AVM) and real-world deployment of machine learning models LangChain: Proficient in using LangChain for building LLM-powered applications Data Science Toolkit: Hands-on with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter Feature Engineering: Strong background in feature engineering and data intelligence extraction Data Handling: Comfortable with structured, semi-structured, and unstructured data Production Integration: Experience integrating models into APIs and production environments using Python-based frameworks Additional Info Generally, should have worked with a different, varied set of data in multiple projects, and problem-solving acumen Requirements AI/ML Models: Experience with Automated Valuation Models (AVM) and real-world deployment of machine learning models LangChain: Proficient in using LangChain for building LLM-powered applications Data Science Toolkit: Hands-on with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter Feature Engineering: Strong background in feature engineering and data intelligence extraction Data Handling: Comfortable with structured, semi-structured, and unstructured data Production Integration: Experience integrating models into APIs and production environments using Python-based frameworks Show more Show less

We're Hiring: AI DevOps Engineer – ML, LLM & Cloud for Battery & Livestock Intelligence 📍 Hyderabad / Remote | 🧠 3–8 Years Experience 💼 Full-time | Deep Tech | AI-Driven IoT At Vanix Technologies , we’re solving real-world problems using AI built on top of IoT data — from predicting the health of electric vehicle batteries to monitoring livestock behavior with BLE sensors. We’re looking for a hands-on AI DevOps Engineer who understands not just how to build ML/DL models, but also how to turn them into intelligent cloud-native services . If you've worked on battery analytics or sensor-driven ML , and you're excited by the potential of LLMs + real-time IoT — this is for you. What You’ll Work On 🔋 EV Battery Intelligence Build models for SOH, true SOH, SOC, RUL prediction , thermal event detection, and high-risk condition classification. Ingest and process time-series data from BMS, CAN bus, GPS , and environmental sensors. Deliver analytics that plug into our BatteryTelematicsPro SaaS for OEMs and fleet customers. 🐄 Livestock Monitoring AI Analyze BLE sensor data from our cattle wearables (motion, temp, rumination proxies). Develop models for health anomaly detection, estrus prediction, movement patterns , and outlier behaviors. Power actionable insights for farmers via mobile dashboards and alerts. 🤖 Agentic AI & LLM Integration Chain ML outputs with LLMs (e.g., GPT-4, Claude) using LangChain or similar frameworks . Build AI assistants that summarize events, auto-generate alerts, and respond to user queries using both structured and ML-derived data. Support AI-powered explainability and insight generation layers on top of raw telemetry. ☁️ Cloud ML Engineering & DevOps Deploy models on AWS (Lambda, SageMaker, EC2, ECS, CloudWatch). Design and maintain CI/CD pipelines for data, models, and APIs. Optimize performance, cost, and scalability of cloud workloads. ✅ You Must Have Solid foundation in ML/DL for time-series / telemetry data Hands-on with PyTorch / TensorFlow / Scikit-learn / XGBoost Experience with battery analytics or sensor-based animal behavior prediction Understanding of LangChain / OpenAI APIs / LLM orchestration AWS fluency: Lambda, EC2, S3, SageMaker, CloudWatch Python APIs Nice to Have MLOps stack (MLFlow, DVC, W&B) BLE signal processing or CAN bus protocol parsing Prompt engineering or fine-tuning experience Exposure to edge-to-cloud model deployment Why Vanix Technologies? Because we're not another AI lab — we're a deep-tech company building production-ready AI platforms that interact with real devices in the field , used by farmers, OEMs, and EV fleets . You’ll work at the intersection of: IoT + AI + LLMs Hardware + Cloud Mission-critical data + Everyday impact Show more Show less

Job Summary: We are looking for a highly motivated and analytical Data Scientist / Machine Learning (ML) Engineer / AI Specialist with 1.5 -2 years of experience in Health data analysis, particularly with data sourced from wearable devices such as smartwatches and fitness trackers. The ideal candidate will be proficient in developing data models, analyzing complex datasets, and translating insights into actionable strategies that enhance health-related applications. Key Responsibilities: Develop and implement data models tailored to health data from wearable devices. Stay updated on industry trends and emerging technologies in health data analytics. Ensure data integrity and security throughout the analysis process , correlations relevant to health metrics. Analyze large datasets to extract actionable insights using statistical methods and machine learning techniques. Develop, train, test, and deploy machine learning models for classification, regression, clustering, NLP, recommendation, or computer vision tasks. Collaborate with cross-functional teams including product, engineering, and domain experts to define problems and deliver solutions. Design and build scalable ML pipelines for model development and deployment. Conduct exploratory data analysis (EDA), data wrangling, feature engineering, and model validation. Monitor model performance in production and iterate based on feedback and data drift. Stay up to date with the latest research and trends in machine learning, deep learning, and AI. Document processes, code, and methodologies to ensure reproducibility and collaboration. Required Qualifications: Bachelor's or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related field. 1.5-2 years of experience in data analysis, preferably within the health tech sector. Strong knowledge of Python or R and libraries such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, or XGBoost. Strong experience with data modeling, machine learning algorithms, and statistical analysis. Familiarity with health data privacy regulations (e.g., HIPAA) and data visualization tools (e.g., Tableau, Power BI). Proficiency in SQL and experience working with large-scale data systems (e.g., Spark, Hadoop, BigQuery, Snowflake). Ability to clearly communicate complex technical concepts to both technical and non-technical audiences. Experience with version control tools (e.g., Git) and ML pipeline tools (e.g., MLflow, Airflow, Kubeflow). Experience deploying models in cloud environments (AWS, GCP, Azure). Knowledge of NLP (e.g., Transformers, LLMs), computer vision, or reinforcement learning. Familiarity with MLOps, CI/CD for ML, and model monitoring tools. Experience - 1.5 - 2 years (Only Local Candidates) Location - Mohali Phase 8b Show more Show less

AI Agent Development - Python (CrewAI + LangChain) Location: Noida / Gwalior (On-site) Experience Required: Minimum 3+ years Employment Type: Full-time 🚀 About the Role We're seeking a AI Agent Developer (Python) with hands-on experience in CrewAI and LangChain to join our cutting-edge AI product engineering team. If you thrive at the intersection of LLMs, agentic workflows, and autonomous tooling — this is your opportunity to build real-world AI agents that solve complex problems at scale. You’ll be responsible for designing, building, and deploying intelligent agents that leverage prompt engineering, memory systems, vector databases, and multi-step tool execution strategies. 🧠 Core Responsibilities Design and develop modular, asynchronous Python applications using clean code principles. Build and orchestrate intelligent agents using CrewAI: defining agents, tasks, memory, and crew dynamics. Develop custom chains and tools using LangChain (LLMChain, AgentExecutor, memory, structured tools). Implement prompt engineering techniques like ReAct, Few-Shot, and Chain-of-Thought reasoning. Integrate with APIs from OpenAI, Anthropic, HuggingFace, or Mistral for advanced LLM capabilities. Use semantic search and vector stores (FAISS, Chroma, Pinecone, etc.) to build RAG pipelines. Extend tool capabilities: web scraping, PDF/document parsing, API integrations, and file handling. Implement memory systems for persistent, contextual agent behavior. Leverage DSA and algorithmic skills to structure efficient reasoning and execution logic. Deploy containerized applications using Docker, Git, and modern Python packaging tools. 🛠️ Must-Have Skills Python 3.x (Async, OOP, Type Hinting, Modular Design) CrewAI (Agent, Task, Crew, Memory, Orchestration) – Must Have LangChain (LLMChain, Tools, AgentExecutor, Memory) Prompt Engineering (Few-Shot, ReAct, Dynamic Templates) LLMs & APIs (OpenAI, HuggingFace, Anthropic) Vector Stores (FAISS, Chroma, Pinecone, Weaviate) Retrieval-Augmented Generation (RAG) Pipelines Memory Systems: BufferMemory, ConversationBuffer, VectorStoreMemory Asynchronous Programming (asyncio, LangChain hooks) DSA / Algorithms (Graphs, Queues, Recursion, Time/Space Optimization) 💡 Bonus Skills Experience with Machine Learning libraries (Scikit-learn, XGBoost, TensorFlow basics) Familiarity with NLP concepts (Embeddings, Tokenization, Similarity scoring) DevOps familiarity (Docker, GitHub Actions, Pipenv/Poetry) 🧭 Why Join Us? Work on cutting-edge LLM agent architecture with real-world impact. Be part of a fast-paced, experiment-driven AI team. Collaborate with passionate developers and AI researchers. Opportunity to build from scratch and influence core product design. Show more Show less

We are looking for an experienced and results-driven Data Scientist with a strong background in machine learning to join our analytics and AI team. Title : Senior Data Scientist Location: Noida ( Sector 62 ) and Kolkata ( New Town ) Experience: 5 to 8 years Shift : Rotational shifts The ideal candidate must have hands-on experience in building, deploying, and optimizing machine learning models to solve real-world problems and drive business value. Key Responsibilities: * Design, develop, and deploy machine learning models and algorithms to address business challenges and opportunities. * Analyze large and complex datasets to extract actionable insights using statistical and ML techniques. * Collaborate with data engineers, analysts, and product teams to implement data-driven strategies. * Evaluate model performance and iterate based on feedback and new data. * Stay current with the latest ML trends, tools, and research to drive innovation. * Present findings and model results to technical and non-technical stakeholders. Skills: * Proficiency in Python (preferred) or R; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, etc. * Strong understanding of supervised, unsupervised, and reinforcement learning methods. Please share your resume at @trishita.mistry@empaxis.com Show more Show less

We’re seeking a skilled Data Scientist with expertise in SQL, Python, AWS SageMaker , and Commercial Analytics to contribute to Team. You’ll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions. This role is ideal for a problem-solver passionate about turning data into strategic value. Key Responsibilities Model Development: Build, validate, and deploy machine learning models (e.g., recommendation engines, propensity models) using Python and AWS SageMaker to drive next-best-action decisions. Data Pipeline Design: Develop efficient SQL queries and ETL pipelines to process large-scale commercial datasets (e.g., customer behavior, transactional data). Commercial Analytics: Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities. Cross-functional Collaboration: Partner with marketing, sales, and product teams to align models with business objectives and operational workflows. Cloud Integration: Optimize model deployment on AWS, ensuring scalability, monitoring, and performance tuning. Insight Communication: Translate technical outcomes into actionable recommendations for non-technical stakeholders through visualizations and presentations. Continuous Improvement: Stay updated on advancements in AI/ML, cloud technologies, and commercial analytics trends. Qualifications Education: Bachelor’s/Master’s in Data Science, Computer Science, Statistics, or a related field. Experience: 3-4 years in data science, with a focus on commercial/customer analytics (e.g., pharma, retail, healthcare, e-commerce, or B2B sectors). Technical Skills: Proficiency in SQL (complex queries, optimization) and Python (Pandas, NumPy, Scikit-learn). Hands-on experience with AWS SageMaker (model training, deployment) and cloud services (S3, Lambda, EC2). Familiarity with ML frameworks (XGBoost, TensorFlow/PyTorch) and A/B testing methodologies. Analytical Mindset: Strong problem-solving skills with the ability to derive insights from ambiguous data. Communication: Ability to articulate technical concepts to business stakeholders. Preferred Qualifications AWS Certified Machine Learning Specialty or similar certifications. Experience with big data tools (Spark, Redshift) or ML Ops practices. Knowledge of NLP, reinforcement learning, or real-time recommendation systems. Exposure to BI tools (Tableau, Power BI) for dashboarding. Show more Show less

Job Description: We are seeking an experienced Data Scientist with expertise in advanced machine learning techniques to join our dynamic team. The ideal candidate will have hands-on experience developing and deploying models using ensemble methods and other cutting-edge ML algorithms, mostly in the US banking domain. Key Responsibilities: Develop and help deploy advanced machine learning models, including ensemble techniques, for customer lifecycle use cases (e.g., prescreen, acquisition, account management, collections, and fraud). Collaborate with cross-functional teams to define, develop, and improve predictive models that drive business decisions. Work with large datasets, utilizing tools like Python and SQL , to extract, clean, and transform data for modeling purposes. Ensure model robustness, interpretability, and scalability within banking environments. Strong problem-solving skills with the ability to handle complex datasets and turn them into actionable insights.

Data Scientist with experience in projects related to Churn- Machine learning-random Forest-XGBoost-Logistics Regression. candidate must be an immediate Joiner or 30 Days NP

Job Description Job summary: Our Firmwide Risk Function is focused on cultivating a stronger, unified culture that embraces a sense of personal accountability for developing the highest corporate standards in governance and controls across the firm. Business priorities are built around the need to strengthen and guard the firm from the many risks we face, financial rigor, risk discipline, fostering a transparent culture and doing the right thing in every situation. We are equally focused on nurturing talent, respecting the diverse experiences that our team of Risk professionals bring and embracing an inclusive environment. Chase Consumer & Community Banking serves consumers and small businesses with a broad range of financial services, including personal banking, small business banking and lending, mortgages, credit cards, payments, auto finance and investment advice. Consumer & Community Banking Risk Management partners with each CCB sub-line of business to identify, assess, prioritize and remediate risk. Types of risk that occur in consumer businesses include fraud, reputation, operational, credit, market and regulatory, among others. Join our Model Insights Team , a Center of Excellence within Consumer & Community Banking (CCB) Risk Modeling, committed to tracking of comprehensive health of machine learning models. We are responsible for sanity of model inputs and score performance tracking for CCB risk decision models. Team collaborates with model developers to identify and recommend potential opportunities for model calibration. We are constantly seeking for opportunities to enhance model performance tracking framework, with aim of providing feedback loop to risk strategies. We are seeking candidates who possess extensive knowledge of data science techniques, appreciation for data combined with of domain expertise, and a keen eye for detail and logic. It’s an opportunity to make an impact to model performance monitoring and governance practices for CCB risk models. Job Responsibilities Drive synergy in model performance tracking across different sub-lines of business. Enhance model performance framework to holistically capture model health, providing actionable insights to model users. Collaborate with model developers to identify potential opportunities for model calibration and conduct preliminary Root Cause Analysis in case of model performance decay. Design and build robust framework to monitor quality of model inputs. Explore opportunities to drive efficiency in model inputs and performance tracking through use of Large Language Model (LLM). Partner with teams across, Risk, Technology, Data Governance, and Control to support effective model performance management and insights. Deliver regular updates on model health to senior leadership of risk organization and the first line of defense. Required Qualifications, Capabilities, And Skills Advanced degree in Mathematics, Statistics, Computer Science, Operations Research, Econometrics, Physics, or a related quantitative field. Minimum of 3 years of experience in developing and managing predictive risk models in financial industry. Proficiency in programming languages such as Python, PySpark, and SQL, along with familiarity with cloud services like AWS SageMaker and Amazon EMR. Deep understanding of advanced machine learning algorithms (e.g. Decision Trees, Random Forest, XGBoost, Neural Networks, Clustering etc) Strong conceptual understanding of performance metrics used to monitor health of machine learning models. Fundamental understanding of the consumer lending business and risk management practices. Experience of working with large datasets with strong ability to analyze, interpret, and derive insights from data. Advanced problem-solving and analytical skills, with a keen attention to detail. Excellent communication skills, with the ability to convey complex information clearly and effectively to senior management. Preferred Qualifications, Capabilities, And Skills Experience of data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). Proven expertise in designing, building, and deploying production-quality machine learning models. Ability to effectively collaborate with multiple stakeholders on projects of strategic importance, ensuring alignment and successful outcomes. Basic level of proficiency in Tableau ABOUT US JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. About The Team Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes. Show more Show less

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. As part of Team Amex, you'll experience this powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. Function Description: The ‘Prospect Direct Mail Analytics’ team is part of the Analytics, Investments and Marketing Enablement (AIM) team within Global Commercial Services Marketing, American Express. AIM team is responsible for targeting, acquiring, engaging, and retaining commercial customers over online and offline channels and delivering world-class analytics, insights and data products for the Global Commercial Services (GCS) business. In this role, the incumbent will lead the Prospects Direct Mail Analytics team within AIM. Purpose of the Role: The Analyst will own the end-to-end analytics required for executing Commercial Prospects Direct Mail campaigns and would be responsible to profitably drive growth in commercial acquisition and charge volume through the Direct Mail channel. The Analyst will be challenged with designing and creating world class prospect marketing analytics solutions by leveraging machine learning and advanced methodologies. The person will be responsible for performing strategic analyses, synthesizing conclusions, and communicating recommendations to partners aimed at driving revenue through acquisitions. The ideal candidate can drive strategic decision making and execute new strategies via advanced analytics, disciplined test & learn, and effective partnership. The position is part of a highly collaborative environment, interacting with and influencing partners across the Global Commercial Services business at American Express. Responsibilities: Drive profitable acquisitions in Direct Mail channel by meeting ROI / Acquisition / Revenue goals with optimization, experimentation and analytics driven insights. Define, Design, Create, and Implement data science & analytical solutions required throughout the life cycle of a Direct Mail campaign starting from lead generation all the way to performance measurement. Collaborate with stakeholders within GCS Prospect marketing, Finance and investment optimization on various initiatives including setting goals for the channel / influencing data-driven strategy changes / introducing offer personalization etc. Researching and evaluating new commercial data sources working with external data vendors to improve data quality. Creating data segmentation & optimization strategies for targeting profitable prospects with the right product/incentive in the Direct Mail channel Translate business problems into Machine Learning problems. Collaborate with Decision Science teams to quantitatively determine the value of ML models, and ensure key insights are leveraged to create the most suitable ML models to solve the business problems. Collaborate with ML and Tech teams to manage, guide and build analytical solutions to improve targeting efficiency in Direct Mail channel. Minimum Qualifications: Bachelor's degree in quantitative field (e.g. Mathematics, Computer Science, Physics, Engineering, Finance and Economics). Demonstrated ability to lead cross-functional teams directly or indirectly to achieve key business outcomes. Strong programming skills are required. Experience with BIG DATA PROGRAMMING LANGUAGES (HIVE, PIG, SPARK), PYTHON (or R or JAVA). Expertise or ability to pick up strong SQL skills. Strong technical and analytical skills with the ability to apply both quantitative methods and business skills to create insights and drive results, such as A/B testing analysis. Strong analytical/conceptual thinking acumen to solve unstructured and complex business problems and articulate key findings to senior leaders/stakeholders in a succinct and concise manner. Demonstrated ability to work independently and across a matrix organization partnering with capabilities, marketing, decision sciences, risk teams and external vendors to deliver solutions at top speed. Preferred Qualifications: Master’s in quantitative field (e.g. Mathematics, Computer Science, Physics, Engineering, Finance and Economics) or MBA with quantitative background. Strong knowledge of machine learning techniques, including XGBoost, Decision Trees and NLP models. Knowledge of commercial data experience is a plus. We back our colleagues and their loved ones with benefits and programs that support their holistic well-being. That means we prioritize their physical, financial, and mental health through each stage of life. Benefits include: Competitive base salaries Bonus incentives Support for financial-well-being and retirement Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location) Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need Generous paid parental leave policies (depending on your location) Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) Free and confidential counseling support through our Healthy Minds program Career development and training opportunities American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations. Show more Show less