Data Scientist

• Design, develop, and deploy machine learning and generative AI models for business needs
• Work with large-scale datasets using Python, SQL, and industry-standard libraries
• Develop and fine-tune LLM-based solutions for real-world applications
• Ensure data quality, integrity, compliance with best-practice frameworks, and coding standards
• Collaborate with engineering, product, and business teams to translate requirements into AI-driven solutions
• Apply NLP techniques for text understanding, summarization, and sentiment analysis
• Implement scalable and efficient solutions on Azure, AWS, or GCP
• Research and experiment with Generative AI, Agentic AI models, and advanced frameworks

Assistant Manager – Data Scientist

• Design and develop data models and analytical solutions
• Optimize data analysis processes and methodologies
• Ensure data solutions meet business and technical requirements
• Provide technical support for data science projects
• Collaborate with stakeholders to address data science needs
• Build statistical models and analyze large datasets
• Present findings to stakeholders
• Collaborate with other data professionals to solve complex problems and improve business outcomes

• Lead architectural design and implementation of multi-agent AI systems
• Drive technical strategy for GenAI initiatives and recommend best practices
• Mentor and provide technical guidance to junior and mid-level engineers
• Collaborate with stakeholders to define requirements and deliver solutions
• Own end-to-end delivery of complex, production-scale AI systems
• Build and maintain high-performance REST/WebSocket APIs using FastAPI and Pydantic v2
• Implement and optimize agentic AI systems using LangGraph, Deep Agents, AutoGen, and LangChain
• Architect real-time, event-driven microservices using messaging queues such as Apache Kafka
• Design clean, testable, maintainable services using SOLID principles, Python async, and type hints
• Integrate and optimize SQL, NoSQL, and vector databases including Postgres, MongoDB, ChromaDB, and Pinecone
• Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls
• Use LLM observability and evaluation tooling such as LangSmith and Langfuse
• Apply LLM safety guardrails including prompt-injection mitigation, PII handling, and content moderation
• Stay current with emerging trends in GenAI, deep learning, and AI orchestration frameworks
• Apply practices aligned with Banking, Insurance, and Healthcare compliance requirements

• Implement machine learning models using platform, client, and public data sources
• Implement data enrichment and cleansing routines
• Implement features and prepare modeling datasets, including feature selection
• Evaluate candidate models, select final models, and report test performance
• Deploy models at runtime and monitor model inputs and performance for stability
• Work with product, sales, and engineering teams to shape solutions
• Analyze data patterns, formulate and test hypotheses, and iterate
• Help prepare sales materials and estimate hardware requirements
• Attend online and onsite client meetings to discuss functionality

Senior Data Scientist II – Machine Learning, Deep Learning, Python, Pytorch

• Solve complex time series forecasting problems and derive actionable insights from terabytes of data
• Use data science tools and techniques to solve Walmart business problems
• Develop proofs of concept, present solutions to business stakeholders and evolve them
• Convert solutions into pipelines or APIs as needed
• Research, learn, disseminate and adapt new technologies
• Improve existing solutions
• Brainstorm and review data science solutions
• Mentor junior associates
• Collaborate with stakeholders in Bentonville to address business challenges
• Build forecasting, prediction and root-cause algorithms
• Develop time series forecasting models and machine learning pipelines in a big data ecosystem

Staff Data Scientist

• Support the PLI and RLI processes for ICS Pay Over Time, including ongoing execution, monitoring, analysis and reporting.
• Perform detailed analysis of PLI/RLI performance and key business metrics to identify trends, opportunities, anomalies and areas requiring further investigation.
• Support the development, implementation and ongoing management of PLI/RLI strategies and initiatives with relevant partners and stakeholders.
• Develop and maintain reporting, dashboards and performance tracking to support business decisions and recommendations.
• Conduct deep-dive analysis of business performance, customer behavior and the impact of changes to PLI/RLI strategies or processes.
• Support testing, implementation and post-implementation monitoring of changes and enhancements, identifying opportunities for process improvement and automation.
• Partner with Product, Risk, Technologies, Operations and other cross-functional stakeholders to support initiatives and resolve issues.
• Ensure PLI/RLI activities follow established policies, procedures, controls and regulatory requirements, maintaining process and control documentation.
• Identify emerging trends, issues or process gaps and communicate findings and recommendations.
• Manage multiple priorities and deliverables while maintaining accuracy and attention to detail.

Data Science Analyst – Strategy

• Own the causal read on core revenue retention and add-on monetization across CPaaS, AI add-ons, and other revenue surfaces
• Quantify add-on revenue opportunity across CPaaS and emerging AI features, and identify drivers behind attach and consumption
• Apply causal inference methods including matching, diff-in-diff, survival/hazard, and synthetic control
• Partner with Finance/RevOps on single-source-of-truth definitions and forecasting inputs
• Partner with Product Strategy & Growth on the TTP/churn charter and with Experimentation to test retention interventions
• Advise Customer Success, Finance, and Communications/CPaaS leaders
• Set analytical standards for data science and analysts on adjacent teams
• Set technical direction for company-wide revenue-retention measurement and own canonical GRR/NRR, churn, and add-on metrics
• Shape the retention analytics taxonomy with Analytics Engineering
• Build reusable retention and causal-inference frameworks
• Use Claude and similar AI tooling for exploration, documentation, and analysis

Staff Data Scientist – Core Revenue Retention

• Own the customer-growth outcome end to end across onboarding, activation, trial-to-paid, multi-product adoption, and expansion revenue
• Identify early and mid-lifecycle behaviors that predict expansion and turn findings into a prioritized growth agenda
• Connect product signals to GTM/marketing spend and Finance growth targets
• Model the B2B2C dynamic of agency-to-sub-account activation and expansion
• Partner on rigorous growth experiments and distinguish causal from correlational claims
• Partner with Product Strategy & Growth on TTP/churn and add-ons charters
• Set company-wide technical direction for customer-growth measurement, canonical metrics, segment definitions, and value models
• Build reusable growth-measurement and causal-inference methods
• Translate findings into decision-grade guidance for Growth, GTM, Finance, and product leaders
• Advise Growth, GTM, and Finance leaders and set analytical standards for adjacent teams
• Flag data gaps to Analytics Engineering and shape the event taxonomy
• Use AI tooling such as Claude to accelerate exploration, documentation, and analysis

Staff Data Scientist – Growth & Expansion

• Own a hard, well-defined merchandising data science problem end-to-end, from business question through production system
• Define decision variables, recommendation logic, objectives, constraints, fallback behavior, and success criteria
• Determine whether to build an automated decision engine or decision-support system
• Select and implement optimization, heuristics, simulation, forecasting, causal/statistical inference, or hybrid approaches
• Build production systems integrated with merchandising, planning, and platform systems
• Establish methods, patterns, and quality standards for other data scientists and engineers
• Define metrics and run experiments to validate improvements in accuracy, forecast error, plan executability, margin, sell-through, or recommendation adoption
• Create documentation, reusable components, and decision frameworks
• Identify the next important iteration of the problem
• Use AI-accelerated development tools while maintaining correctness and maintainability
• Communicate problems, tradeoffs, recommendations, and implications to engineering leads, merchants, and business stakeholders

• Conduct patent and trademark portfolio audits
• Migrate portfolios into SaaS-based software using Excel or similar tools
• Manage efficient and on-time project delivery with high quality standards
• Update software databases
• Communicate with clients and attorneys over conference calls as required
• Keep abreast of commercial IP law updates
• Understand customer requirements and provide and implement improvement ideas

Data Scientist jobs in India

Assistant Manager – Data Scientist

Data Scientist

Data Scientist

Senior Data Scientist II – Machine Learning, Deep Learning, Python, Pytorch

Staff Data Scientist

Data Science Analyst – Strategy

Staff Data Scientist – Core Revenue Retention

Staff Data Scientist – Growth & Expansion

Data Scientist

Team Lead – Data Validation, Japanese Speaking