Machine Learning Engineer - Optimization & Insights (Retail)

Profitmind is building the intelligence behind how retailers make pricing and merchandising decisions. Today, many of these decisions are still driven by spreadsheets, rigid rules, and manual judgment, even at the largest brands.

Our platform turns complex data such as sales, inventory, and competitive signals into clear, explainable recommendations merchants can trust. Our platform focuses on impact, helping retailers improve margin, inventory health, and decision quality at scale.

Based in Pittsburgh, Profitmind is backed by a recent strategic investment from Accenture, and scaling its agentic AI platform to power decision-making for some of the world’s largest retailers.

About the role

We are seeking a Machine Learning Engineer who understands the heartbeat of retail. In this role, you will build the intelligence that helps major retailers make critical merchandising decisions, balancing profit, revenue, and inventory health.

You will sit at the intersection of ML Engineering, Retail Strategy, and Data Science. Your work will power the "brain" of our platform, transforming raw sales data and competitive signals into actionable pricing strategies and clear insights for merchants and buyers.

What you’ll do:

• Design and deploy models specifically for retail challenges, such as demand forecasting, price elasticity at the SKU level, seasonality detection, and markdown optimization.
• Evolve our Python-based optimization engine to handle complex retail constraints (e.g., maintaining brand standards, psychological price points, and inventory sell-through targets).
• Engineer systems that explain the "why" behind a price change. You will translate model outputs into merchant-friendly insights (e.g., "We recommend a markdown here because competitor X dropped price and inventory depth is high").
• Develop logic to optimize products across their entire lifecycle—from initial price setting to promotional strategies and final clearance.
• Build robust data pipelines to ingest and process diverse retail datasets, including POS transactions, competitor scraping, and inventory feeds.
• Work closely with product managers to understand the needs of category managers and pricing analysts, ensuring our algorithms solve real-world merchandising pain points.

What we’re looking for:

• 3+ years of experience building production ML systems using Python, Scikit-learn, or PyTorch, with a focus on regression and time-series forecasting.
• A strong understanding of (or deep interest in) retail mechanics—how inventory turns, gross margin, and sell-through rates drive business success.
• Familiarity with mathematical optimization techniques and how to apply them to business constraints (e.g., linear programming, constraint satisfaction).
• Expert SQL skills and ability to model complex data relationships (e.g., parent-child product hierarchies, store clusters).
• The ability to look at an optimization result and explain the business logic behind it. You can debug not just code, but the retail logic.
• Bachelor’s degree in Computer Science, Data Science, Machine Learning, Mathematics, or a related technical field.

Nice to have

• Master’s degree in Artificial Intelligence, Computer Science, Operations Research, or Statistics.
• Experience in Retail Analytics, E-commerce, Supply Chain, or Revenue Management.
• Familiarity with "Explainable AI" (XAI) tools to make black-box models transparent to business users.
• Experience handling sparse data or cold-start problems (e.g., pricing new products with no history).