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Data Scientist, Product to Market (P2M) Optimization
SF - 2 Folsom
Live, first seen by us 14 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
Before you apply
- New graduates
- Entry level
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
What you’ll work on
Full postingDevelop 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
Develop and test machine learning, optimization, and statistical models for retail P2M problems
Analyze large datasets and build analytical solutions using Python and SQL
Write reliable, maintainable code and contribute to testing and documentation
Evaluate model performance and measure business impact with statistical methods
Work with senior data scientists to troubleshoot, improve, and operationalize solutions
From the employer’s posting
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
What you’ll bring
All qualificationsCore experience
- 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
- Hands-on proficiency in Python and SQL
Preferred experience
- Experience with mathematical optimization, including linear or mixed-integer programming, and tools such as Gurobi, Pyomo, OR-Tools, or equivalent
- Familiarity with experimentation or causal methods for measuring business impact
Qualification wording
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
Hands-on proficiency in Python and SQL
Experience with mathematical optimization, including linear or mixed-integer programming, and tools such as Gurobi, Pyomo, OR-Tools, or equivalent
Familiarity with experimentation or causal methods for measuring business impact
Education & alternatives
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
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 21, 202614 days ago
- Recorded sightings
- 31
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Gapinc is hiring
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Databricks
- PySpark
Where the posting names them
- 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
- 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
- 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
Your next step
Complete your application on gapinc.wd1.myworkdayjobs.com. The employer’s form will show what is required.
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Already applied? Track this application
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
SF - 2 Folsom
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.