738814-Business Analyst/Subcon

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 Position Name Req ID Experience Location Duration Budget Business Analyst 738814 3-6 Years Remote 6 Months 17 USD/ Hour

Business Analyst – Trade Promotion Optimization (TPO) Purpose: Own the business-to-data science hand-off for promo investments, ensuring pricing, discounts, trade terms, budgets, margins, cannibalization rules, POS thresholds and customer constraints are complete, validated and optimization-ready before model build, simulation and solver runs.

Experience: 3–6 years Role scope • Act as the commercial bridge between business teams and Data Scientists for TPO cycles. • Collect and validate promo investment inputs: pricing, discounts, promo combos, trade terms, baseline sales, costs, margins, and budgets. • Translate commercial requirements into model-ready templates, constraint rules and optimization payloads. • Coordinate clarifications with category, sales, finance, RGM and customer teams before optimization runs. Relevant work areas • Revenue Growth Management, Trade Promotion Management, or Trade Promotion Optimization. • Category management, sales finance, commercial planning, demand planning or retailer analytics. • Nielsen/NIQ, retailer EPOS, customer P&L, promo calendars, pricing files and master data experience preferred.

Key responsibilities • Maintain input templates, data dictionaries, business rules and readiness checklists for each optimization cycle. • Pre-check data quality: missing fields, duplicate master codes or PPG mappings, inconsistent price or discount logic, week/date alignment and outliers. • Validate business constraints: brand budget, max promos, weeks between promos, customer margin, POS sales and cannibalization matrix rules. • Document assumptions, signoffs, unresolved questions, change logs and rationale for any pre-modelling adjustments. • Support interpretation of outputs such as selected promotions, iGP, iNTS, K_ROI, budget utilization, and business trade-offs.

Skills required • Strong FMCG/CPG commercial analytics understanding across pricing, promotions, retailer investments and gross margin. • Advanced Excel and structured data QA; Python, SQL and Databricks preferred. • Ability to understand optimization basics: objective functions, constraints, feasibility, solver diagnostics, and promotional calendars. • Excellent stakeholder communication, documentation discipline and business-to-technical translation. Success measures • More optimization runs through pass input validation on first submission. • Fewer business clarification loops for Data Scientists. • Clear traceability from business assumptions to modelling inputs and final recommendations.

why this person is needed

1. Creates clear ownership for converting commercial promo intent into validated, optimization-ready inputs.
2. Reduces time Data Scientists spend on data gathering, formatting, validation and repeated business follow-ups.
3. Improves optimization quality by catching gaps in pricing, discounts, budgets, cannibalization and constraints before modelling starts.
4. Builds business confidence through transparent assumptions, documented signoffs and clearer explanation of promotional recommendations.
5. Lets the Data Science team focus on algorithms, model accuracy, diagnostics and scenario optimization rather than upstream data wrangling.