Senior Director, Data Science - AI Agent Development

About the Role

The Senior Director of Data Science - AI Agent Development will lead the strategic and technical vision for Gap Inc.’s AI Agent ecosystem — a suite of generative and autonomous systems powering customer engagement, merchandising, and supply chain optimization.

This leader will oversee a cross-functional organization of data scientists, machine learning engineers, and AI researchers, developing agentic systems that reason, plan, and act across the enterprise. You’ll work closely with senior executives, digital product, and technology teams to drive measurable business value through intelligent automation and human-AI collaboration.

• Strategic Leadership — Define and execute the AI Agent strategy across Gap Inc.’s brands, aligning with enterprise digital transformation goals and customer experience objectives.
• Strategic Leadership — Build and scale an AI Agent platform that supports customer service, digital styling, inventory intelligence, and creative design automation.

• Technical and Organizational Leadership — Lead a multidisciplinary team of data scientists, research engineers, and applied ML experts, focused on reinforcement learning, LLM orchestration, and multi-agent systems.
• Technical and Organizational Leadership — Architect systems that combine retrieval-augmented generation (RAG), autonomous planning, tool use, and memory for scalable AI Agents across retail functions.
• Technical and Organizational Leadership — Oversee Agent infrastructure, and experimentation frameworks to accelerate model deployment and iteration.
• Technical and Organizational Leadership — Partner with product and platform engineering to ensure scalable APIs, model governance, and observability for AI Agents in production.
• Technical and Organizational Leadership — Drive innovation and research partnerships with the office of AI to stay at the frontier of agentic AI and generative reasoning.
• Technical and Organizational Leadership — Partner with leadership to integrate AI-driven decisioning and co-pilot experiences across Enterprise and Product to Market.
• Technical and Organizational Leadership — Establish frameworks for responsible and explainable AI, ensuring models align with brand values, diversity, and data privacy standards.
• Technical and Organizational Leadership — Shape the long-term vision for human-AI collaboration, including associate enablement tools and in-store intelligence.

• Define and own key AI Agent performance metrics
  , including automation rate, customer engagement lift, and ROI on operational efficiency.
• Champion data-driven experimentation and A/B testing methodologies for AI-driven workflows.
• Create dashboards and narratives that translate AI impact into business outcomes for executive stakeholders.

• Technical Expertise — 10+ years of experience in applied data science, AI research, or machine learning engineering, including 5+ years leading large teams.
• Technical Expertise — Proven experience in LLM development, multi-agent systems, or intelligent automation (e.g., Lang Chain, OpenAI API, Anthropic, or custom orchestration frameworks).

• Strategic and Leadership Skills — Experience defining and executing AI strategy within a global consumer brand.
• Strategic and Leadership Skills — Exceptional ability to translate technical complexity into clear business value narratives.
• Strategic and Leadership Skills — Track record of building diverse, high-performing AI organizations.
• Strategic and Leadership Skills — Deep commitment to ethical AI practices and customer trust.
• Strategic and Leadership Skills — Strong collaborator with engineering, digital product, and marketing leadership.
• Strategic and Leadership Skills — Strong understanding of deep learning, reinforcement learning, retrieval systems, and natural language processing.
• Strategic and Leadership Skills — Hands-on knowledge of modern ML infrastructure (e.g., Databricks, AWS/GCP, Vertex AI, MLflow, Ray, Hugging Face).
• Strategic and Leadership Skills — Expertise in data architecture, feature engineering, and experimentation pipelines at enterprise scale.