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Gap Inc.Data Scientist, Product to Market (P2M) Optimization
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Before you apply
SalaryNo pay range posted
LocationSF - 2 Folsom; NY 55 Thomas St
Work moderemote · United States
Typefull-time
SeniorityEntry
Experience2–3 yrs
Company size10,000+ people
PostedOct 20, 2026 · 2w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Master's degree in Operations Research, Operations Management, Industrial Engineering, Applied Mathematics/Statistics, Data Science, or a related quantitative field
2 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 SQL
Self-motivated with demonstrated initiative and the ability to operate effectively in a fast-paced, evolving environment
Strong analytical, communication, and collaboration skills
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 language models
Skills
Python
SQL
Gurobi
Pyomo
OR-Tools
PySpark
Databricks
GCP
Git
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 at scale. The 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
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Develop and test machine learning, optimization, and statistical models for retail P2M problems
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Analyze large datasets and develop analytical solutions using Python, SQL, and related technologies
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Write reliable, maintainable code and contribute to testing, documentation, and code management
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Evaluate model performance and support measurement of business impact using appropriate statistical methods
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Work with senior data scientists to troubleshoot, improve, and operationalize analytical solutions
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Collaborate with business, product, engineering, and data science partners to understand requirements and communicate analytical findings
Who You Are
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Master’s degree in Operations Research, Operations Management, Industrial Engineering, Applied Mathematics/Statistics, Data Science, or a related quantitative field
•
2 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 SQL
•
Self-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 language models