Re-joined Amazon to lead the newly formed central Pricing & Promotions Science, Data, and Insights organization, raising the science innovation bar, and applying a combination of applied machine learning, stochastic optimization, reinforcement learning, causal modeling, counterfactual measurement, and generative AI, to grow and maximize Customer, Seller, and Amazon success through optimal application of pricing and promotions levers. 

Our portfolio of achievements included:

• Launched an LLM-infused product price range estimation system for Retail pricing & channel-agnostic price quality assessment

• Deployed a causal Long Term Value forecasting and attribution model to quantify incremental sales value from various pricing policies & actions

• Developed an LLM-based hyper-parallel price-informative product attribute extraction and normalization stack (90%+ cost reduction)

• Launched counterfactual promotional sales forecasting model for deals sourcing and prioritization (15-20% reduced forecast error & bias)

• Conceived and prototyped a stochastic-optimization system for promotional event success maximization (5-8%+ sales lift)

• Launched a multi-armed bandit dynamic parameter calibration system Retail price matching (+tens $M financials, across all metrics)

• Launched new adaptive promotional lever elasticity and matching system (+2% improved financials)

• Developed and deployed a vastly improved pricing experimentation & post-lab analytics ecosystem (90%+ speedup, 70% saved manual effort)

• Guided the development of a central ML Ops hosting and deployment platform

02/2025 - 11/2025 : Leading Pricing & Promotions Science and Data

11/2024 - 02/2025 : Leading Pricing & Promotions Science, and 1P+3P Pricing Data

03/2024 - 11/2024 : Leading Pricing & Promotions Science, and 3rd Party Seller (3P) Pricing Data

Built and grew the Sciences organization at Equilibrium Energy, comprising Machine Learning, Power Systems, Operations Research & Optimization, Quantitative Trading, and Analytics. My applied research teams a) develop the autonomous ML & Optimization powered forecasts and algorithms responsible for operating and maximizing revenue from industrial-sized batteries in the US electricity markets, and b) create & operate novel power market convergence bidding trading strategies, achieving exceptional realized sharpe and sortino ratios. Reporting to the CEO

04/2023 - 02/2024 : Promoted to lead of all Equilibrium’s sciences as a core member of EQ’s Senior Leadership Team.

02/2022 - 03/2023 : Leading Data Science, Machine Learning, and Quantitative Analytics

Led machine learning and reinforcement learning science initiatives for Central Retail Pricing. My group developed models, solutions, and platforms to drive the price competitiveness, improved customer perception, and increased long term value of product prices for over $XXB of annual retail product revenue worldwide. 

In under two years, my teams:

• Re-designed and scaled our elasticity & reinforcement learning based dynamic pricing system for private brand and core retail products WW

• Developed and deployed a synthetic counterfactual financial metrics tracking & reporting framework

• Devised and aligned multiple VP-level orgs around novel initiatives and a 3-5 year science vision for Amazon pricing

• Identified gaps and suggested scientific improvements in Central Pricing's A/B testing platform

• Improved ML-driven pricing models and price competitiveness by between 50%-75% across 9+ countries

• Generated over $0.XB incremental annual long term free cashflow through science driven pricing enhancements & improved customer pricing

03/2021 - 02/2022 : Base Pricing, Private Brands, Competitor Matching

05/2020 - 02/2021 : Base Pricing, Private Brands

For several years, I actively participated in crowd sourced machine learning contests on the Kaggle platform, placing in the money on multiple occasions, and ultimately earning their competitions Grand Master status. At peak, I was ranked in the top 10 participants on the overall competitions platform globally. Although retired since early 2018, I am still an avid proponent of the benefits of the platform, and continue to monitor and engage with the wider community.