Data Science Enablement Lead — Adoption & Impact

Gap Inc.; San Francisco (CA); On-site; USD 118,000 - 153,000; Full time

Benefits offered by this job

Merchandise discount for brands

Paid time off

401(k) matching

Employee stock purchase plan

Medical, dental, vision insurance

Job summary

Gap Inc. seeks a data-driven strategist to drive adoption of AI and ML models across its brands. You will act as an internal consultant within the Data Science organization, translating complex model outputs into actionable business narratives for merchants, planners, and sourcing leaders.

The role emphasizes structured thinking, reliable delivery, and clear communication over pure coding. You will build and maintain a library of model explainability briefs, design adoption experiments, and

Qualifications

• 3–6 years in management consulting, customer success, or client-facing analytics
• Excellent written and verbal communication skills
• Ability to manage multiple senior stakeholder relationships with reliability
• Familiarity with retail processes is a meaningful advantage

Responsibilities

• Own end-to-end model adoption lifecycle across DS portfolio
• Maintain model explainability cards with business-language summaries
• Design adoption-focused experiments and translate results to business impact
• Build adoption dashboards tracking coverage, influence, and time-to-adoption
• Produce quarterly business impact reports quantifying margin lift and forecast accuracy
• Synthesize stakeholder feedback and relay priorities to DS team

Skills

Model adoption

Stakeholder management

Data literacy

MLOps familiarity

SQL proficiency

Python proficiency

Tools

SQL

Python

Tableau

Looker

Power BI

Excel

Job description

Gap Inc. seeks a data-driven strategist to drive adoption of AI and ML models across its brands. You will act as an internal consultant within the Data Science organization, translating complex model outputs into actionable business narratives for merchants, planners, and sourcing leaders.

The role emphasizes structured thinking, reliable delivery, and clear communication over pure coding. You will build and maintain a library of model explainability briefs, design adoption experiments, and