Master's degree in a quantitative field such as Operations Research or Data Science
2-3 years of experience in data science or applied analytics
Experience with retail, supply chain, or pricing problems
Strong foundation in machine learning and statistics
Proficiency in Python and SQL
Self-motivated and able to thrive in a fast-paced environment
Excellent analytical and communication skills
Responsibilities
Develop and test machine learning and optimization models for retail problems
Analyze large datasets and create analytical solutions using Python and SQL
Write reliable and maintainable code, contributing to documentation and testing
Evaluate model performance and measure business impact using statistical methods
Collaborate with senior data scientists to enhance analytical solutions
Work with cross-functional teams to understand requirements and share findings
Benefits
Opportunity to work in a fast-paced, evolving environment
Hands-on experience with real-world retail problems
Collaboration with experienced data scientists and cross-functional partners
Development of expertise in applied data science and optimization
Potential for career growth within a leading retail organization
Full Job Description
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
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 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
