Senior Analyst, Data Science Enablement

Gap Inc.•San Francisco, CA

About The Position

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.

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
Comfort operating in 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
Proficiency in 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-agency work style: proactively identifies blockers, manages up clearly, and brings a proposed solution alongside every problem

Nice To Haves

Familiarity with MLOps concepts (model cards, drift monitoring, override analytics) is a plus
Experience partnering with Data Science or Engineering teams in a previous role is an advantage

Responsibilities

Own the end-to-end adoption lifecycle for a portfolio of DS models — from first stakeholder introduction through sustained, broad-based use
Own and maintain a 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 and maintain adoption 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 — identifying where and why humans deviate from model recommendations and routing structured findings back to model owners
Build and maintain trusted relationships with business partners across Merchandising, Merchandise Planning, and Inventory 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
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, what doesn't, and how to accelerate adoption for future model launches