Data & AI Consulting Manager / Senior Manager in Consumer & Retail

You understand the Consumer & Retail industry: its commercial dynamics, margin pressures, operational challenges, and complex decision-making processes.

You've seen data and AI initiatives fail to deliver, whether through strategies that never reached implementation, models that were never adopted, or dashboards that answered the wrong questions. You want to bridge the gap between business needs and practical solutions, working closely with technical teams to ensure ideas become outcomes.

As a Data & AI Consultant within our Consumer & Retail practice, you will work with clients from initial discovery through to implementation. You'll help shape proposals, build business cases, lead workstreams, and act as the link between senior client stakeholders and EPAM's engineering and data science teams.

Your focus will be on advanced analytics and AI use cases, including forecasting, pricing and elasticity modeling, attribution, and scenario simulations. While you won't develop the models yourself, you'll be responsible for ensuring the right solutions are designed, aligned to business objectives, and capable of delivering measurable value.

Shape pursuits and proposals: contribute to presales from the start — defining the problem, scoping the approach, and building EPAM’s value narrative for Consumer & Retail clients Lead client discovery: facilitate workshops with commercial, category and supply chain leadership, frame the real problem — not just the stated one — and define what a good outcome looks like Build the business case: identify, prioritise and size data and AI use cases; translate them into business cases with KPIs, investment rationale and delivery roadmaps that get sign-off Own the workstream end-to-end: requirements, backlog, data readiness, governance and value tracking through to adoption Brief the builders: work closely with Data Engineers, Data Scientists and Solution Architects to design scalable solutions — you define the what and why, they build the how Tell the story: produce clear, executive-level materials and present recommendations to leadership throughout the engagement Pricing & promotions: elasticity modelling, promotional effectiveness, trade spend optimisation, net revenue management Merchandising & category: assortment optimisation, product performance, markdown and clearance Customer & growth: segmentation, CLV, churn/retention, personalisation, next-best-action Demand & supply chain: demand forecasting and sensing, replenishment, availability and fulfilment analytics GenAI-enabled use cases: commercial copilots for category managers and account managers, knowledge and insight agents, automation of analysis and reporting Deep Consumer & Retail expertise — whether built in-industry or through sustained consulting work with retailers, CPG/FMCG, eCommerce or wholesale clients — with concrete examples of measurable business impact Consulting experience: structured problem-solving, stakeholder management, workshop facilitation and executive-level communication Data and AI literate: able to define data requirements and KPIs, hold a substantive conversation with a data scientist or architect, and translate between business and technical audiences End-to-end agile delivery experience: from problem framing and business case through to delivery and adoption into a recurring planning or commercial process, with a clear view of what worked and what didn’t Fluent in CPG data landscape: syndicated and panel data (NIQ/Nielsen, Circana, Kantar), retailer POS and portal feeds, distributor sell-out, and the practical experience working with them Comfortable with ambiguity takes ownership and drives to outcomes without waiting to be told what the answer is Familiarity with major data cloud platforms (Databricks, Snowflake) and hyperscalers (Azure, GCP or AWS) and how data and AI solutions are architected and deployed on them Experience with advanced analytics or GenAI patterns (forecasting, recommendation engines, optimisation, LLMs, RAG) in a Consumer & Retail context Hands-on exposure to the modelling families behind this work — hierarchical and time-series forecasting, econometric and elasticity models, optimisation, causal inference and incrementality testing Knowledge of enterprise platforms common in Consumer & Retail environments, such as SAP, Salesforce or major ERP and CRM systems Change management and adoption skills: ensuring that solutions land and are actually used, not just delivered Experience in data governance, data quality frameworks or target operating model design is a plus Multi-market experience — working across a regional or global CPG operating model, with the data and process differences that come with it