AI Business Analyst Lead - Retail and Consumer Goods

Business Analyst

Project Role : Business Analyst
Project Role Description : Analyze an organization and design its processes and systems, assessing the business model and its integration with technology. Assess current state, identify customer requirements, and define the future state and/or business solution. Research, gather and synthesize information.
Must have skills : Data Analytics
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

As an AI Business Analyst Lead for Retail and Consumer Goods, you will bridge the gap between business strategy and AI technology — translating client challenges into actionable AI use cases, intelligent data products, and agentic workflow designs. You will act as the primary point of contact between business stakeholders and AI engineering teams, driving requirements, shaping AI roadmaps, and ensuring delivered solutions generate measurable business value for clients undergoing AI transformation.

Roles & Responsibilities
Lead AI use case discovery, requirements gathering, and solution design for Retail and CPG clients across demand forecasting, personalisation, supply chain, RGM, Route to Marlet & marketing analytics.
Translate complex business problems into structured AI/ML problem statements, data requirements, and acceptance criteria for engineering teams.
Define and document agentic AI workflows — mapping business processes to autonomous agent capabilities such as pricing optimization, inventory management, and customer retention.
Collaborate with AI Data Engineers and Data Scientists to validate LLM-powered solutions align with business intent, data governance policies, and ROI expectations.
Design and evaluate prompt engineering strategies and RAG-based knowledge systems that serve business users through AI copilots and intelligent dashboards.
Act as SME for Retail/CPG domain — mentor junior analysts, drive solution decisions, and engage C-level and operational stakeholders across business and technology.
Build and maintain business cases, AI product roadmaps, and delivery artefacts — user stories, functional specifications, process maps, and KPI frameworks.
Ensure responsible AI adoption — communicating model explainability, bias risks, compliance requirements, and change management needs to business stakeholders.

Technical & Business Skills
Must Have:
Strong Retail and CPG domain knowledge — merchandising, supply chain, customer analytics, promotions, and demand planning.
AI/ML literacy — ability to understand, evaluate, and communicate LLM capabilities (GPT-4, Claude, Gemini) and their business applicability without deep coding.
Experience designing AI use cases involving RAG pipelines, vector search, and conversational AI for business workflows.
Requirements engineering for AI products — writing user stories, defining training data needs, and setting evaluation criteria for ML models.
Business process mapping and gap analysis to identify AI automation and augmentation opportunities.
Stakeholder management — ability to run workshops, facilitate design thinking sessions, and align diverse teams around AI solution priorities.
Good to Have:
Hands-on familiarity with prompt engineering, LLM APIs, or no-code AI tools (Azure OpenAI Studio, Copilot Studio).
Understanding of agentic AI frameworks (LangChain, AutoGen, MCP) to effectively collaborate with engineering teams on agent design.
Exposure to data platforms — Azure, Snowflake, Databricks — at a functional level for scoping and feasibility assessment.
Knowledge of responsible AI principles — explainability, fairness, compliance — and how to embed them into business requirements.
Experience with agile delivery frameworks (Scrum, SAFe) in AI or data product environments.

Additional Information
Minimum 7.5 years in Business Analysis or Product roles, with at least 3 years working directly on AI/ML or data product delivery in Retail or CPG.
Demonstrated ability to translate ambiguous business problems into delivered AI solutions with measurable outcomes — not just documentation.
Experience working in cross-functional AI delivery teams alongside data engineers, data scientists, and cloud architects is required.