Data Scientist, Ads Demand

OpenAI•San Francisco, CA

2d•$293,000 - $515,000

About The Position

OpenAI's ads business is scaling quickly. As the Data Scientist for Ads Demand, you will build the measurement and insight foundation for understanding demand health and advertiser value across the marketplace. You will partner closely with Ads Sales leadership and Ads Product leadership—along with Marketing Science and product and sales teams—to diagnose advertiser performance, define benchmarks, identify growth opportunities, and turn advertiser feedback into product priorities. Your work will shape demand strategy, improve advertiser outcomes, and help OpenAI build for its most valuable advertisers.

Requirements

7+ years of experience in data science or analytics within an ads platform, marketplace, or performance-oriented B2B business.
Demonstrated business impact through advertiser demand growth, improved delivery or ROAS, stronger retention, or decisions that changed product and sales strategy.
Strong SQL and Python or R, with depth in measurement, experimentation, causal inference, segmentation, benchmarking, and forecasting.
Exceptional cross-functional communication and influence with both sales and product leaders, plus a hands-on approach to ambiguous, 0-to-1 problems.

Nice To Haves

Experience at a high-performing ads business where data scientists work broadly across Sales, Marketing Science, and Product rather than in a narrow silo.
Hands-on familiarity with ad delivery, auctions, measurement, attribution, conversion signals, and the advertiser lifecycle.
Experience turning customer feedback into product priorities and evidence-backed best practices that improve advertiser outcomes.

Responsibilities

Define the North Star metrics, diagnostic framework, and measurement strategy for demand health across the ads system.
Build the operating view of demand across spend, active advertisers, retention, budget utilization, delivery, concentration, and mix; identify emerging risks and opportunities.
Diagnose changes in demand through cohort analysis, decomposition, experimentation, and causal methods, translating findings into clear actions for Sales and Product leadership.
Own the end-to-end view of advertiser outcomes—including delivery, ROAS, conversion performance, retention, and budget efficiency—for individual advertisers and key cohorts.
Establish actionable benchmarks by objective, vertical, advertiser size, geography, maturity, and product adoption, with statistically sound peer comparisons.
Develop early-warning signals and opportunity scoring that help sales teams surface under-delivery, performance risk, and advertiser growth potential.
Set standards for metric definitions, data quality, and interpretation so leaders can separate real marketplace changes from seasonality, selection effects, and measurement artifacts.
Partner with Marketing Science, Sales, and Product to translate analysis into credible advertiser-facing insights, benchmarks, and best practices.
Design measurement plans and experiments that quantify how best-practice and product adoption affect delivery, ROAS, retention, and long-term advertiser value.
Build a systematic feedback loop that converts advertiser input into quantified themes and prioritized product opportunities, then measures whether shipped changes improve outcomes.
Partner with Ads Sales leadership to segment demand, size opportunities, forecast outcomes, and inform demand strategies, sales plays, and investments.
Partner with Ads Product leadership to estimate advertiser value, prioritize the roadmap, and measure the business impact of product launches.