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About This Role
Data Science - San Francisco
About the role
We’re looking for a product data scientist to help OpenAI create more value and drive sustainable growth for young people, families, and learners. You’ll drive product data science across under-18 audiences, emerging opportunities for children under 13, family experiences, and learning and education more broadly.
You’ll help decide where we should invest, what we should build, and how we’ll know it is working. This means understanding distinct audience needs, identifying promising use cases, and turning evidence into a clear product strategy. Your work will connect discovery and early product development with adoption, retention, and sustained user value.
You’ll partner across Chat Product, Growth, Foundations, SafeSys, and other teams, working closely with product managers, engineers, designers, and researchers. The role focuses and owns product value and growth while dedicated teams own policy and safety.
In this role, you will:
- Shape strategy and prioritize opportunities. Size unmet needs and growth opportunities across audiences, use cases, and product experiences. Help teams choose where to invest and sequence work based on expected user value, reach, evidence, and effort.
- Define what value and success mean. Develop an audience-specific measurement framework spanning activation, task success, learning progress, satisfaction, and durable retention. Connect these measures to product goals and assess whether repeat use reflects meaningful benefit.
- Understand users and growth drivers. Analyze journeys, funnels, and cohorts to uncover where people find value, where they struggle, and what helps them return. Combine behavioral data with surveys and user research to explain differences across ages, family roles, and learning contexts.
- Measure what changes actually cause. Design experiments and holdouts to evaluate product and model changes. Apply causal methods when randomization is impractical, make assumptions explicit, and account for seasonality, audience composition, and effects that differ across segments.
- Turn findings into product decisions. Work with partners from initial hypotheses through launch and iteration. Recommend what to build, change, expand, or stop, explaining expected impact, uncertainty, and tradeoffs. Follow through to determine whether decisions delivered the intended results.
- Build reliable measurement. Partner with engineering on instrumentation, dataset quality, and consistent audience definitions. Create reusable analyses and dashboards that make progress visible and let teams answer recurring questions independently.
- Create alignment across teams. Bring a coherent view of audience value to work spanning multiple roadmaps. Help partners agree on priorities, success criteria, and the next questions to resolve, even when ownership is distributed and evidence is incomplete.
You might thrive in this role if you have:
- A record of influencing product strategy and improving user outcomes through hands-on data science, including work from an ambiguous question to a decision and measurable impact.
- Strong SQL and Python skills, sound statistical judgment, and practical experience with experiments, causal inference, cohort analysis, and measurement design.
- The ability to develop useful definitions of product value, validate imperfect proxies, and distinguish short-term metric movement from lasting improvement.
- Clear communication and strong product judgment. You can make a recommendational authority.
- Comfort setting priorities independently across a broad scope, staying close to users, and moving between strategic questions and detailed analytical work.
Helpful experience
Experience with consumer product growth, youth or family products, learning and education, or generative AI would be valuable. We also welcome experience measuring learning outcomes, combining quantitative and qualitative research, or evaluating products whose value emerges over time.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core the full spectrum of humanity.
We are an equal opportunity employeration, or other applicable legally protected characteristic.
Background checks for applicants will be administered in accordance with applicable lawation technology systems and related data security obligations.
To notify OpenAI that you believe this job posting is non-compliant. No response will be provided to inquiries unrelated to job posting compliance.
We are committed to providing reasonable accommodations to applicants with disabilities.
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Compensation
$401K – $510K + Offers Equity
The base pay offered may vary depending on multiple individualized factorsance-related bonus(es) for eligible employees, and the following benefits.
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.
More details about our benefits are available to candidates during the hiring process.
This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.
Salary Context
This $401K-$510K range is above the 75th percentile for Data Scientist roles in our dataset (median: $162K across 195 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At OpenAI, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($455K) sits 140% above the category median. Disclosed range: $401K to $510K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
OpenAI AI Hiring
OpenAI has 25 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Architect. Positions span New York, NY, San Francisco, CA, US, San Francisco, CA. Compensation range: $280K - $510K.
Location Context
AI roles in San Francisco pay a median of $260,550 across 2,766 tracked positions. That's 22% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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