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