{"id":2958868,"url":"https://alion.io/job/gap-inc-data-scientist-product-to-market-p2m-optimization","title":"Data Scientist, Product to Market (P2M) Optimization","company":{"id":12256,"name":"Gap Inc","domain":"gapinc.com","url":"https://alion.io/company/gap","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":76,"open_postings":32,"ghost_share":0,"stale_share":0.969,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Folsom, United States","United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":78000,"max_usd":167000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":376},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AI Agents","optional":true},{"name":"Databricks","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-20T22:20:19Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-11-05T19:36:45Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"About the Role\nGap 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.\nWhat You'll Do\nDevelop and test machine learning, optimization, and statistical models for retail P2M problems\nAnalyze large datasets and develop analytical solutions using Python, SQL, and related technologies\nWrite reliable, maintainable code and contribute to testing, documentation, and code management\nEvaluate model performance and support measurement of business impact using appropriate statistical methods\nWork with senior data scientists to troubleshoot, improve, and operationalize analytical solutions\nCollaborate with business, product, engineering, and data science partners to understand requirements and communicate analytical findings\nWho You Are\nMaster’s degree in Operations Research, Operations Management, Industrial Engineering, Applied Mathematics/Statistics, Data Science, or a related quantitative field\n2 to 3 years of experience in data science, machine learning, operations research, or applied analytics\nExperience working on retail, supply chain, pricing, inventory, merchandising, or related business problems\nStrong foundation in machine learning, statistics, and quantitative problem solving\nHands-on proficiency in Python and SQL\nSelf-motivated with demonstrated initiative and the ability to operate effectively in a fast-paced, evolving environment\nStrong analytical, communication, and collaboration skills\nPreferred Qualifications\nExperience with mathematical optimization, including linear or mixed-integer programming, and tools such as Gurobi, Pyomo, OR-Tools, or equivalent\nExposure to PySpark, Databricks, GCP, Git, or production data science environments\nFamiliarity with experimentation or causal methods for measuring business impact\nExposure to building AI agents or agentic workflows using large language 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