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OpenAI

Builds AI models and the ChatGPT assistant

Data Scientist - Ads Demand

Data ScienceConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$293k - $515k /yrIn-person

About the Job

Requirements

  • At least 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 return on ad spend (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.
  • Cross-functional communication and influence with sales and product leaders, and a hands-on approach to ambiguous, zero-to-one problems.

Responsibilities

  • Define North Star metrics, a 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 to help sales teams surface under-delivery, performance risk, and advertiser growth potential.
  • Set standards for metric definitions, data quality, and interpretation so leaders can distinguish 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 to quantify how best practices 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.

Desired Qualifications

  • 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.

About the Company

OpenAI

Builds AI models and the ChatGPT assistant

OpenAI researches and builds artificial intelligence models and turns them into products for people, developers, and businesses, with a stated mission of making sure artificial general intelligence benefits all of humanity. Its launch of ChatGPT in 2022 brought generative AI into everyday use. People use ChatGPT to ask questions, write, code, and analyze files, developers build OpenAI's models into their own apps through its API, and its Codex agent works through coding tasks on its own. OpenAI earns money from ChatGPT subscriptions, business plans, and API usage. Sam Altman, Elon Musk, and others co-founded it in 2015; it is based in San Francisco and overseen by a nonprofit, the OpenAI Foundation.

Company size
1,001-5,000
Company stage
Late Stage VC
Total funding
$207.5B
Headquarters
San Francisco, CA
Founded
2015

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