Lead Data Scientist

About the Role

We are looking for a Lead Data Scientist – Agentic AI to lead the design, development, and productionization of next-generation Agentic AI, Generative AI, Machine Learning, and Advanced Analytics solutions for real-world business problems.

The ideal candidate will combine strong foundations in Statistics, Machine Learning and Data Science with hands-on expertise in LLMs, AI Agents, agentic workflows, RAG, tool calling, multi-agent systems, and AI application engineering.

This is a hands-on technical leadership role. The Lead Data Scientist will be expected to take ownership of complex problems end-to-end — from understanding the business problem and defining the analytical approach to designing the AI architecture, building and evaluating solutions, deploying them to production, and mentoring other Data Scientists.

Prior experience in forecasting, demand planning, supply chain, pricing, promotions, or other CPG/Retail analytics will be an added advantage.

Key Responsibilities

1. Agentic AI & Generative AI

• Lead the design and development of production-grade AI agents and agentic workflows.
• Design agent architectures involving LLMs, tools, APIs, databases, business systems, and external services.
• Build and orchestrate multi-agent systems for complex analytical and business workflows.
• Design agent capabilities including tool calling, memory, state management, planning, reasoning and workflow execution.
• Build enterprise-grade RAG solutions and retrieval architectures.
• Develop frameworks to evaluate AI systems for accuracy, groundedness, reliability, consistency and business relevance.
• Identify and address hallucinations, failure modes and reliability issues in agentic applications.
• Evaluate emerging Agentic AI technologies and determine their practical application to business problems.

2. Data Science & Machine Learning

• Lead the application of Statistics, Machine Learning, predictive modelling and optimization to complex business problems.
• Design analytical approaches for large and complex datasets.
• Develop, validate and optimize ML models.
• Establish robust model validation and performance measurement frameworks.
• Translate analytical outputs into actionable business recommendations.
• Guide Data Scientists in selecting appropriate modelling approaches based on business objectives and data characteristics.

3. Forecasting & CPG/Retail Analytics – Preferred

• Apply forecasting and predictive analytics techniques to business problems involving:

oDemand forecasting

oSupply chain & inventory

oPricing & promotions

oCategory management

oShopper/customer analytics

• Experience with time-series forecasting, demand planning or CPG/Retail analytics will be a strong advantage.
• Guide the team in developing scalable forecasting and predictive solutions.

4. AI Solution Architecture & Engineering

• Design end-to-end AI architectures covering Data Science, LLMs, agents, APIs, databases and deployment infrastructure.
• Develop backend services using Python, Fast API or equivalent frameworks.
• Integrate ML models, LLMs, databases, APIs and external tools.
• Review technical implementations for scalability, maintainability and performance.
• Establish reusable engineering patterns and best practices for AI applications.

5. Production & Cloud

• Take AI/ML solutions from prototype to production.
• Containerize applications using Docker.
• Deploy AI solutions on AWS, Azure or GCP.
• Work with Platform/DevOps teams on scalable AI architecture.
• Contribute to CI/CD, monitoring, logging and production reliability.
• Diagnose production issues and drive improvements in system performance and reliability.

6. Technical Leadership & Mentoring

• Own complex AI problems from problem definition through production.
• Break ambiguous business problems into executable technical components.
• Mentor and guide Data Scientists and AI Engineers.
• Review modelling approaches, AI architectures and technical implementations.
• Establish technical standards and reusable components.
• Collaborate with Data Engineering, Software Engineering and DevOps teams.
• Work directly with business stakeholders and clients when required.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline.
• 8+ years of experience in Data Science, Machine Learning, AI, GenAI or related areas.
• Strong foundation in Statistics, Machine Learning and Data Science.
• Strong hands-on experience with Python and SQL.
• Significant experience with Generative AI / LLM applications.
• Strong understanding and practical experience with:

oRAG

oEmbeddings

oTool calling

oAgent orchestration

oAgent memory/state

oLLM evaluation

oAI agents / agentic workflows

• Experience designing and building production-grade AI systems.
• Experience developing APIs/backend services using FastAPI or similar frameworks.
• Experience with relational databases such as PostgreSQL.
• Hands-on experience with Docker and at least one cloud platform – AWS, Azure or GCP.
• Strong problem-solving and analytical ability.
• Ability to independently own complex technical problems and drive them to completion.
• Demonstrated ability to mentor and technically guide other Data Scientists.

Preferred Skills:

• Forecasting / Time-Series Modelling
• CPG / Retail / Supply Chain experience
• Client-facing experience

Good to have Skills:

• LangGraph / LangChain or equivalent frameworks
• Advanced RAG architectures
• Vector databases
• Multi-agent systems
• Guardrails and AI reliability
• LLMOps / MLOps / GenAIOps
• Kubernetes / ECS / EKS
• Redis / Kafka / RabbitMQ / Celery
• Terraform
• Enterprise AI applications / AI portals