Enterprise Retail & supply chain

Markdown Optimization

Lead Data Scientist / Technical Lead · H-E-B

Price-elasticity forecasting and a constrained optimizer that recommends multi-week discount schedules merchants adopt as-is.

The problem

Markdowns are a trade-off between revenue, shrink and empty shelves, and they were set by rule of thumb across three retail channels.

Led the price-elasticity component on Databricks/PySpark: an ensemble of XGBoost, LightGBM and AdaBoost with monotonic constraints, trained on four years of store-SKU POS history to produce 38-week demand curves.
Led the optimization engine: a pruned combinatorial search over multi-week discount schedules that balances revenue against shrink and empty-shelf cost, with channel-specific weights, hard inventory caps and merchant guardrails.
Delivered weekly recommendations that merchants typically adopt as-is, with overrides in the UI.

Enterprise Retail & supply chain

A 22-repository GCP platform forecasting SKU-store sales and promotional redemption for ~350 stores over an 89-week horizon.

~50% lower WMAPE vs. legacy

Enterprise Retail & supply chain

Response-curve forecasting plus mixed-integer optimization that decides how much shelf each product section gets in every store.

−86% runtime (9 h → 75 min)

Enterprise Retail & supply chain

A 21-day, hourly forecast of orders and units for every fulfillment-center and store pair, feeding staffing and capacity planning.

~14% WMAPE in staging

Open to consulting, advisory roles and select projects.

I work directly with teams to design, build and ship AI, forecasting and optimization systems — and stay through rollout.