Data Scientist, Product to Market; P2M) Optimization

Position: 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.

About the role

Gap Inc. is seeking a Data Scientist to join our Product-to-Market (P2M) Data Science team. P2M encompasses the decisions that shape how products move from planning to the customer, including what products to carry, how much inventory to buy, where and how inventory should be positioned, and how products should be priced and promoted. Our team applies machine learning, optimization, and analytics to improve these decisions across P2M lifecycle.

This is a hands-on role for someone early in their career who has strong quantitative and programming fundamentals and wants to solve real-world retail problems  team operates in a fast-paced environment where priorities evolve and problems are not always fully defined. Success requires curiosity, initiative, and self-motivation, with a willingness to take ownership, learn quickly, and proactively move work forward. The candidate will work with experienced data scientists and cross-functional partners while building deeper expertise in applied data science and optimization.

What you'll do Develop and test machine learning, optimization, and statistical models for retail P2M problems

Analyze large datasets and develop analytical solutions using Python, SQL, and related technologies

Write reliable, maintainable code and contribute to testing, documentation, and code management

Evaluate model performance and support measurement of business impact using appropriate statistical methods

Work with senior data scientists to troubleshoot, improve, and operationalize analytical solutions

Collaborate with business, product, engineering, and data science partners to understand requirements and communicate analytical findings

Who you are Master’s degree in Operations Research, Operations Management, Industrial Engineering, Applied Mathematics/Statistics, Data Science, or a related quantitative field2 to 3 years of experience in data science, machine learning, operations research, or applied analytics

Experience working on retail, supply chain, pricing, inventory, merchandising, or related business problems

Strong foundation in machine learning, statistics, and quantitative problem solving

Hands-on proficiency in Python and SQLSelf-motivated with demonstrated initiative and the ability to operate effectively in a fast-paced, evolving environment

Strong analytical, communication, and collaboration skills

Preferred Qualifications

Experience with mathematical optimization, including linear or mixed-integer programming, and tools such as Gurobi, Pyomo, OR-Tools, or equivalent

Exposure to PySpark, Databricks, GCP, Git, or production data science environments

Familiarity with experimentation or causal methods for measuring business impact

Exposure to building AI agents or agentic workflows using large…