Principal Data Scientist : Product to Market; P2M Optimization

About Gap Inc.

At Gap Inc., we create culture as much as we create clothes. Our ambition is to become a high‑performing house of iconic American brands that shape culture. Our portfolio—Old Navy, Gap, Banana Republic, and Athleta—each brings a distinct point of view to how we show up in the world and serve our customers. Old Navy democratizes style with quality and value for all.

Gap champions originality through essential pieces that celebrate individuality. Banana Republic is rooted in a spirit of discovery, creating modern pieces inspired by craftsmanship and travel. Athleta champions the Power of She through confidence, strength, and movement. We’re driven by a shared purpose: to bridge gaps—between people, perspectives, and possibilities—to create a better world. We’re building a team that performs at a high level—people who think boldly, take ownership, and turn ideas into impact.

If you’re ready to learn fast and help shape what’s next, you’ll fit right in.

Gap Inc. is seeking a Principal Data Scientist with deep expertise in operations research and machine learning to lead the design and deployment of advanced analytics solutions across the Product‑to‑Market (P2M) space. This role focuses on driving enterprise‑scale impact through optimization and data science initiatives spanning pricing, inventory, and assortment optimization. The Principal Data Scientist serves as a senior technical and strategic thought partner, defining solution architectures, influencing product and business decisions, and ensuring that analytical solutions are both technically rigorous and operationally viable.

The ideal candidate can lead end‑to‑end solutioning independently, manage ambiguity and complex stakeholder dynamics, and communicate technical and business risk effectively across teams and leadership levels.

• Lead the framing, design, and delivery of advanced optimization and machine learning solutions for high‑impact retail supply chain challenges.
• Partner with product, engineering, and business leaders to define analytics roadmaps, influence strategic priorities, and align technical investments with business goals.
• Provide technical leadership to other data scientists through mentorship, design reviews, and shared best practices in solution design and production deployment.
• Evaluate and communicate solution risks proactively, grounding recommendations in realistic assessments of data, system readiness, and operational feasibility.
• Evaluate, quantify, and communicate the business impact of deployed solutions using statistical and causal inference methods, ensuring benefit realization is measured rigorously and credibly.
• Serve as a trusted advisor by effectively managing stakeholder expectations, influencing decision‑making, and translating analytical outcomes into actionable business insights.
• Drive cross‑functional collaboration by working closely with engineering, product management, and business partners to ensure model deployment and adoption success.
• Quantify business benefits from deployed solutions using rigorous statistical and causal inference methods, ensuring that model outcomes translate into measurable value.
• Design and implement robust, scalable solutions using Python, SQL, and PySpark on enterprise data platforms such as Databricks and GCP.
• Contribute to the development of enterprise standards for reproducible research, model governance, and analytics quality.

• Master’s or Ph.D. in Operations Research, Operations Management, Industrial Engineering, Applied Mathematics, or a closely related quantitative discipline.
• 10+ years of experience developing, deploying, and scaling optimization and data science solutions in retail, supply chain, or similar complex domains.
• Proven track record of delivering production‑grade analytical solutions that have influenced business strategy and delivered measurable outcomes.
• Strong expertise in operations research methods, including linear, nonlinear, and mixed‑integer programming, stochastic modeling, and simulation.
• Deep technical proficiency in Python, SQL, and PySpark, with experience in optimization and ML libraries such as…