Data Scientist, Product to Market (P2M) Optimization

The Gap, Inc.; United States; On-site; USD 110,000 - 140,000; Full time

Job summary

Gap Inc. is seeking a Data Scientist to join our Product-to-Market (P2M) Data Science team. This hands-on role for someone early in their career applies ML, optimization, and analytics to improve catalog decisions, inventory positioning, pricing, and promotions.

This role offers collaboration with cross-functional partners and a fast-paced environment where ownership and proactive problem solving are valued. A Master’s degree and 2–3 years of experience are preferred.

Qualifications

• 2 to 3 years of data science or analytics experience.
• Experience with retail, supply chain, pricing, inventory, or merchandising problems.
• Strong Python and SQL fundamentals.
• Ability to communicate results clearly to technical and business teams.
• Strong analytical and problem-solving skills.

Responsibilities

• 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 measure 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.

Skills

Python

SQL

Machine Learning

Statistics

Communication

Collaboration

Education

Master’s degree in Operations Research / Data Science

Tools

Gurobi

Pyomo

OR-Tools

PySpark

Databricks

GCP

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