Director, Data Science

Gap Inc.•Coppell, TX

About The Position

The Director, Data Science leads a cross-functional Data Science team that builds and deploys AI and machine learning solutions to deliver measurable business impact across Gap Inc. brands. The role combines domain expertise and people-first leadership, setting high standards for analytical rigor, model quality, and stakeholder partnership while fostering an environment where Data Scientists can do their best work. A key focus is leading optimization efforts across assortment, inventory, allocation, pricing, and supply chain. The Director guides the design and deployment of production-ready optimization solutions, helps translate operational constraints and business objectives into actionable models, and drives adoption of recommendations in business decision workflows. The role partners with senior business and technology leaders and is accountable for work delivered with urgency, precision, and commercial impact.

Requirements

At least 7 years of experience in Data Science, Analytics, or a closely related quantitative field.
At least 2 years in a people leadership role managing a team of Data Scientists or Analysts.
Demonstrated expertise in optimization methods and their application in retail or consumer goods, including operations research, constraint-based modeling, and decision-support systems.
Strong proficiency in Python and SQL.
Hands-on experience with optimization libraries such as PuLP, OR-Tools, Gurobi, CPLEX, or SciPy, and with machine learning frameworks.
Proven track record delivering production-grade Data Science or optimization solutions in retail, e-commerce, or supply chain, from problem framing through deployment and adoption.
Strong ability to communicate complex quantitative findings to non-technical business audiences, prepare executive-ready presentations, and influence senior stakeholders.
Experience managing multiple workstreams with clear prioritization, milestone tracking, and stakeholder communication.
Fluency in causal inference and experimentation, including A/B testing, DiD, and Synthetic Control, and the ability to design frameworks that isolate the impact of model recommendations.
Familiarity with retail business processes, particularly Merchandising, Inventory Planning, Allocation, Pricing, or Sourcing, and understanding of how Data Science and optimization create commercial value in these areas.
Ability to coach, develop, and retain Data Science talent, give direct feedback, and navigate performance conversations with care and clarity.
Embodies Gap Inc. Values-Based Leadership Behaviors: Centers on the Customer, Creates with Curiosity, Collaborates with Candor, and Champions Excellence.

Nice To Haves

Familiarity with cloud-based data and compute platforms such as GCP, Databricks, or Snowflake.

Responsibilities

Lead the team with strong domain knowledge and align Data Science projects with business goals, organizational strategy, stakeholder needs, and Gap Inc. strategic priorities.
Establish a culture of rigorous measurement and deliver impactful, scalable, maintainable, and business-aligned solutions.
Encourage research into retail industry, macroeconomic, and consumer trends, and identify business opportunities and risks with recommended actions for brand and GTS leaders.
Lead the design, development, and deployment of optimization models for assortment planning, inventory allocation, replenishment, pricing, and supply chain.
Guide selection and application of optimization approaches based on problem structure, data availability, and business constraints.
Set best practices for optimization model evaluation, scenario analysis, and sensitivity testing so solutions are robust, interpretable, and actionable.
Partner with Merchandising, Inventory Planning, Sourcing, and Pricing teams to define optimization problems from business objectives and operational trade-offs.
Drive business adoption of optimization outputs through clear interfaces with human decision workflows, allowing users to interrogate, override, and trust recommendations.
Coach and guide direct reports in technical and communication skills, stakeholder influence, and professional development.
Communicate complex Data Science insights clearly to technical and non-technical audiences.
Anticipate obstacles and stakeholder resistance, adapt team approaches, and ensure deliverables are completed on time and to standard.
Hold the team accountable for deliverables while maintaining psychological safety and continuous learning.
Build cross-functional relationships and promote a one-team approach within GTS and across the brands.
Guide development of data-driven solutions for complex business problems and remove obstacles to meeting milestones.
Ensure rigorous testing, including A/B tests, holdout groups, and causal measurement approaches, to produce accurate, unbiased, business-aligned insights.
Guide large-scale data projects using appropriate technical tools and languages, aligned with business goals and engineering standards.
Prioritize concurrent projects according to business priorities, timelines, and budget constraints.
Promote sharing and adaptation of research, modeling techniques, and analytical methods to drive measurable improvements.
Establish the team’s credibility as trusted experts in Data Science and optimization across the company.