Gap Inc.
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.
What You'll Do
- 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.
Who You Are
- 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.
Preferred Qualifications
- 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.