Place of work: remote + occasionally one of the EPAM offices in Poland
Contract of employment or B2B contract
You deeply understand Consumer & Retail: commercial mechanics, margin pressures, time-draining processes, and decisions made on gut feel when the data should be driving the strategy. And you've watched data and AI initiatives fall short — strategies that never became products, models that were technically impressive but sat on the shelf, and dashboards that answered the wrong questions.
You want to be the person who closes that gap — who translates a real business problem into a solution that actually gets built and adopted, working alongside engineers and data scientists who can make it happen.
As a Data & AI Consultant in our Consumer & Retail practice, you will be involved from the first client conversation — shaping proposals, leading discovery, building the business case, and owning the workstream that takes an idea to production. You will bridge senior client stakeholders and EPAM’s engineering and data science teams, setting direction and ensuring both sides are pulling in the same direction.
The center of gravity is advanced and predictive analytics combined with AI — spanning forecasting, elasticity modeling, scenario simulations, causal attribution, generative AI use cases, and machine learning models. You will not be building the models yourself, but you will be responsible for whether the right ones are being built — judging whether an approach fits the business question, agreeing on what accuracy is good enough to meet business goals, and defining how it will be measured.
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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
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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
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Build the business case: identify, prioritize and size data and AI use cases; translate them into business cases with KPIs, investment rationale and delivery roadmaps that get sign-off
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Own the workstream end-to-end: requirements, backlog, data readiness, governance and value tracking through to adoption
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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
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Tell the story: produce clear, executive-level materials and present recommendations to leadership throughout the engagement
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Pricing & promotions: elasticity modeling, promotional effectiveness, trade spend optimization, net revenue management
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Merchandising & category: assortment optimization, product performance, markdown and clearance
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Customer & growth: segmentation, CLV, churn/retention, personalization, next-best-action
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Demand & supply chain: demand forecasting and sensing, replenishment, availability and fulfillment analytics
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GenAI-enabled use cases: commercial copilots for category managers and account managers, knowledge and insight agents, automation of analysis and reporting
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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
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Consulting experience: structured problem-solving, stakeholder management, workshop facilitation and executive-level communication
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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
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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
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Fluent in CPG data landscape: transaction/order data, product data, customer data (from CRM systems and loyalty programs), syndicated and panel data (NIQ/Nielsen, Circana, Kantar), retailer POS and portal feeds, distributor sell-out, and the practical experience working with them
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Comfortable with ambiguity: taking ownership and driving to outcomes without waiting to be told what the answer is