Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
About the Role
In this role, you are the bridge between Data Science and the business, responsible for driving the adoption of AI and ML models across Gap Inc. brands. You will operate as an internal consultant, 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, focusing on structured thinking, reliable delivery, and making technical complexity simple and actionable for leaders.
Responsibilities
- Own the end-to-end adoption lifecycle for a portfolio of DS models.
- Own and maintain a library of Model Explainability Cards.
- Design and coordinate adoption-focused A/B tests embedded in production business workflows.
- Build and maintain adoption dashboards that track model coverage, influence rate, override rate, and time-to-adoption.
- 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 and routing structured findings back to model owners.
- Build and maintain trusted relationships with business partners across Merchandising, Merchandise Planning, and Inventory Management.
- Conduct structured discovery with business teams to diagnose adoption barriers and develop tailored enablement plans.
- Develop and deliver executive-ready presentations, model explainability briefs, and quarterly business impact reports.
- 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.
- Serve as the voice of the business back into the DS team, synthesizing stakeholder feedback.
- Partner with business users and DS domain leads to redesign human-AI workflows.
- Train and coach business stakeholders on AI model interpretation, appropriate use, and feedback mechanisms.
- Contribute to the institutional DS Enablement playbook, documenting best practices for adoption.
Requirements
- 3–6 years of experience in management consulting, customer success, or a client-facing analytics role.
- Demonstrated ability to manage multiple senior stakeholder relationships simultaneously.
- Exceptional written and verbal communication skills.
- Comfort operating in ambiguity and framing open-ended business problems.
- Sufficient data literacy to work credibly alongside a Data Science team.
- Proficiency in SQL and/or Python for data pulling and light analytics.
- Experience with Tableau, Looker, Power BI, or equivalent for dashboard development.
- Experience designing and running structured experiments or pilots.
- Familiarity with retail business processes (Merchandising, Inventory Planning, Allocation, or Sourcing) is a meaningful advantage.
- High-agency work style: proactively identifies blockers and proposes solutions.
- Familiarity with MLOps concepts is a plus.
- Experience partnering with Data Science or Engineering teams is an advantage.
Skills
- Structured thinking
- Business enablement
- Commercial narratives
- Model explainability
- A/B testing
- Adoption dashboards
- Business impact reporting
- Override analytics
- Stakeholder management
- Internal consulting
- Executive presentations
- Workshop facilitation
- Workflow redesign
- Change management
- AI model interpretation
- SQL
- Python
- Tableau
- Looker
- Power BI
- Experiment design
- Retail business processes
- MLOps concepts
Experience Level
- 3-6 years