Manager, Pricing Optimization, Old Navy

Gap Inc.•San Francisco, CA

About The Position

Gap Inc. is transforming how its brands use data and AI to make faster, more precise commercial decisions, and the Manager, Pricing Optimization is where that work gets built. This role is the optimization engine of Old Navy’s pricing function—the person who runs the models, builds the scenarios, synthesizes the demand signals, and produces the decision-ready analysis that drives how the brand promotes and marks down product. Reporting to the Sr. Manager, Pricing Optimization, you will own the execution of specific pricing and promotional analytics workstreams, operating with a high degree of technical proficiency and analytical independence. You will apply and validate outputs from pricing agents, build and stress-test scenario models, and distill complex data into clear, well-packaged insights that your team brings to Old Navy’s business owners and finance partners. You will also support the development of pricing analysts and help train pricing agents, playing a key part in building the team’s analytical capabilities as the function grows.

Requirements

Bachelor’s degree required.
6+ years of experience in pricing analytics, demand planning, commercial analytics, or a related field—with direct, hands-on exposure to retail or consumer products pricing.
Strong technical proficiency in building and running pricing models: elasticity analysis, sensitivity and risk modeling, promotional effectiveness, markdown optimization, and scenario simulation.
Experience or an aptitude for working with AI-generated analytical outputs: you know how to apply, interrogate, and quality-check outputs from pricing agents, and you understand the difference between a reliable output and one that needs scrutiny.
Proficiency with reporting and analytics platforms; you use these tools fluently and independently in your day-to-day analytical work.
Demonstrated ability to produce clean, well-structured analytical deliverables that translate complex data into clear commercial recommendations.
Analytically exacting: you hold a high personal bar for accuracy, take ownership of your models and assumptions, and don’t let outputs go forward until you’re confident they’re right.
Commercially curious: you connect your analytical work to business outcomes—margin, revenue, promotional ROI, customer impact—and you develop recommendations that reflect that understanding, not just technical outputs.
Clear and direct communicator: you can distill analytical complexity into plain, well-structured commercial framing that non-analytical partners can read and act on.
AI-partnership mindset: you engage with agentic tools and pricing agents as a critical collaborator—fluent enough to use them effectively, rigorous enough to catch when they’re wrong, and grounded in ethical and brand-aligned judgment.
Curious and adaptable: you bring genuine interest in how pricing, AI, and retail intersect, actively build your skills as the tools evolve, and apply a test-and-learn approach to your own analytical methods.

Responsibilities

Build and Execute Pricing Optimization: Own specific markdown and promotional analytics workstreams for Old Navy—building and running the models, applying elasticity and sensitivity analysis, and producing well-structured recommendations grounded in accurate retail data.
Develop and stress-test AI-scenario simulations for in-season and seasonal pricing decisions, applying rigorous analytical judgment to validate outputs from pricing agents and flag assumptions, inconsistencies, or risks before recommendations are finalized.
Synthesize demand signals and apply predictive pricing insights across assigned categories—including promotional effectiveness, markdown timing, yield management, and good/better/best architecture—to produce analysis that is accurate, thorough, and commercially grounded.
Maintain ongoing monitoring of in-season pricing actions for assigned workstreams, proactively identifying risks or optimization opportunities and escalating with clear data and a recommended point of view.
Apply AI reporting tools and pricing platforms as a proficient daily user—interrogating outputs critically, ensuring data integrity, and knowing when something requires human review before informing a decision.
Prepare Decision-Ready Analysis and Insights: Package analytical work into clear, well-structured deliverables that your team brings to Old Navy business owners and finance partners—making the “what” and “so what” explicit and the trade-offs easy to act on.
Prepare supporting analysis and materials for weekly and monthly business reviews, synthesizing pricing performance across assigned categories into accurate, insight-led summaries.
Translate data complexity into plain commercial framing: you don’t just produce the output—you articulate what it means for margin, revenue, or promotional ROI in language that non-analytical stakeholders can use.
Respond to ad hoc analytical requests from Pricing leaders with speed and accuracy, building a track record as someone who can be relied on to deliver clean, well-reasoned work under time pressure.
Help Build the Future of Pricing: Contribute directly to training and refining Old Navy’s pricing agents—identifying where AI outputs fall short, documenting patterns, and working with data science and technology partners to improve model performance over time.
Help define what good looks like in an agentic pricing workflow—documenting analytical standards, flagging edge cases, and contributing institutional knowledge that shapes how pricing agents are structured, validated, and improved.
Bring a builder’s mindset to a function that is being created from scratch—piloting new approaches, stress-testing what works, and actively contributing to how this team operates as it scales into something that has never existed at Gap Inc. before.