Sr Analyst, Data Science Enablement

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 embedded within 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 actually drives measurable business impact. Success in this role looks less like coding and more like consulting — 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 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

Stakeholder Management & Internal Consulting

Build and maintain trusted relationships with business partners across Merchandising, Inventory Planning, Sourcing, Design, and Costing — 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, what doesn't, and how to accelerate adoption for future model launches

Who You Are

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

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