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Senior Analyst, Data Science Enablement

The Gap

  • Folsom, California
  • 17 days ago

    Highlights

    You will operate as an internal consultant working in partnership with the Data Science organization: building trusted relationships with business stakeholders, translating complex model outputs into clear commercial narratives, and ensuring that every model the DS team builds drives measurable business impact. Sufficient data literacy to work credibly alongside a Data Science team — comfortable with concepts such as model accuracy, confidence intervals, feature importance, A/B testing, and business KPIs; does not need to build models but must be able to interrogate and interpret them.

    Numbers & Facts

    LocationFolsom, California
    IndustryAll
    Company Size10,000 employees or more
    Websitehttp://www.gap.com

    Description

    About the Role

    In this role, you are the bridge between Data Science and the business — responsible for driving adoption of AI and ML models across Gap Inc. brands. You will operate as an internal consultant working in partnership with the Data Science organization: building trusted relationships with business stakeholders, translating complex model outputs into clear commercial narratives, and ensuring that every model the DS team builds drives measurable business impact. Success in this role looks less like coding and more like business enablement — structured thinking, reliable delivery, and the ability to make technical complexity feel simple and actionable to merchant, planning, and sourcing leaders.

    What You'll Do

    Model Adoption & Value Realization

    • Own the end-to-end adoption lifecycle for a portfolio of DS models — from first stakeholder introduction through sustained, broad-based use

    • Own andmaintaina library of Model Explainability Cards — one-page business-language explainers for every production AI/ML model in the DS portfolio

    • Design and coordinate adoption-focused A/B tests embedded in production business workflows, translating experiment results into business-language impact summaries

    • Build andmaintainadoption dashboards that track model coverage, influence rate, override rate, and time-to-adoption by function and brand

    • Produce quarterly per-model business impact reports that quantify margin lift, forecast accuracy improvement, cycle-time reduction, and sell-through impact

    • Diagnose adoption stalls by analyzing override analytics —identifyingwhere and why humans deviate from model recommendations and routing structured findings back to model owners

    Stakeholder Management & Internal Consulting

    • Build andmaintaintrusted relationships with business partners across Merchandising,MerchandisePlanning,andInventory Management— acting as their primary point of contact for all things related to DS model adoption

    • Conduct structured discovery with business teams to diagnose adoption barriers, surface unmet needs, and develop tailored enablement plans by function and brand

    • Develop and deliver executive-ready presentations, model explainability briefs, and quarterly business impact reports for senior stakeholders up to VP and SVP level

    • Facilitate workshops, working sessions, and office hours that bring data science outputs to life for non-technical audiences

    • Proactively manage a portfolio of business relationships — tracking open issues, commitments, and follow-through with a high standard of reliability and responsiveness

    • Serve as the voice of the business back into the DS team, synthesizing stakeholder feedback and routing prioritized signal to model owners and engineers

    Workflow Integration & Change Management

    • Partner with business users and DS domain leads to redesign human-AI workflows so that model recommendations are embedded naturally in existing tools — PLM, planning platforms, costing tools, and sourcing systems — rather than requiring users to change behavior

    • Train and coach business stakeholders on AI model interpretation,appropriate use, and feedback mechanisms; build repeatable onboarding materials that scale across brands

    • Contribute to the institutional DS Enablement playbook, documenting what works, whatdoesn't, and how to accelerate adoption for future model launches

    Who You Are

    Requirements

    • 3–6 years of experience in management consulting, customer success, or a client-facing analytics role; experience in a high-accountability, client-facing or internal consulting function is strongly preferred

    • Demonstrated ability to manage multiple senior stakeholder relationships simultaneously with a high standard of responsiveness, follow-through, and structured communication

    • Exceptional written and verbal communication skills — able to write crisp executive briefs, structure a compelling slide, and present confidently to VP-level audiences without relying on jargon

    • Comfortoperatingin ambiguity: able to take an open-ended business problem, frame it clearly, and drive it to a concrete recommendation or deliverable without constant direction

    • Sufficient data literacy to work credibly alongside a Data Science team — comfortable with concepts such as model accuracy, confidence intervals, feature importance, A/B testing, and business KPIs; does not need to build models but must be able to interrogate and interpret them

    • Proficiencyin SQL and/or Python for pulling data, building adoption metrics, and supporting light analytics; experience with Tableau, Looker, Power BI, or equivalent for dashboard development

    • Experience designing and running structured experiments or pilots, with the ability to interpret results and translate statistical findings into plain-language business impact

    • Familiarity with retail business processes — particularly Merchandising, Inventory Planning, Allocation, or Sourcing — is a meaningful advantage; multi-brand or omnichannel experience is a plus

    • High-agencyworkstyle:proactivelyidentifiesblockers, manages up clearly, and brings a proposed solution alongside every problem

    • Familiarity withMLOpsconcepts (model cards, drift monitoring, override analytics) is a plus; experience partnering with Data Science or Engineering teams ina previousrole is an advantage

    About Company

    Doris and Don Fisher opened the first Gap store in 1969 with a simple idea -- to make it easier to find a pair of jeans and a commitment to do more. Over the last 46 years, the company has grown from a single store to a global fashion business with five brands -- Gap, Banana Republic, Old Navy, Athleta and Intermix. Gap's clothes are available in 90 countries worldwide through 3,300 company-operated stores, almost 400 franchise stores, and e-commerce sites and is still growing. Many companies work to improve their services and businesses every day by using GAP Testers who anonymously go into various places and report back to the companies on everything from cleanliness, customer service to quality control. Being a tester is a very flexible, fun job with lots of benefits.

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