Principal Data Scientist : Product to Market (P2M) Optimization

The Gap•Folsom, NY

12d

About The Position

About the Role 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.

Requirements

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 Pyomo, Gurobi, OR-Tools, scikit-learn, and MLlib.
Hands-on experience with enterprise platforms such as Databricks and cloud environments
Demonstrated ability to assess, communicate, and mitigate risk across analytical, technical, and business dimensions.
Excellent communication and storytelling skills, with a proven ability to convey complex analytical concepts to technical and non-technical audiences.
Strong collaboration and influence skills, with experience leading cross-functional teams in matrixed organizations.
Experience managing code quality, CI/CD pipelines, and GitHub-based workflows.

Nice To Haves

Experience shaping and executing multi-year analytics strategies in retail or supply chain domains.
Proven ability to balance long-term innovation with short-term deliverables.
Background in agile product development and stakeholder alignment for enterprise-scale initiatives.

Responsibilities

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.