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About This Role
Data Science - San Francisco
About the Role
We are hiring a Data Scientist to help build OpenAI's B2B ecosystem and go-to-market data science capability. You will turn ambiguous commercial questions into evidence, recommendations, and operating decisions that help OpenAI understand where to focus as our B2B business expands.
OpenAI's B2B motion is growing quickly across products, customer segments, markets, and field teams. This role will partner with leaders across GTM, Revenue, Product, Finance, RevOps, Strategy, and Data Science to answer questions tied to international strategy, monetization, product penetration, field deployment, customer opportunity, and organizational design.
This is a founding-style opportunity within the broader GTM Data Science organization. The team and stakeholder model are still evolving, so you should be energized by building structure in ambiguity, helping define how the team works, and helping shape the bench around a high-priority commercial area.
In this role, you will bring data science craft, consulting-style problem solving, practical AI fluency, and strong business judgment to help senior leaders make better B2B decisions.
In this role, you will:
- Structure ambiguous B2B and go-to-market questions into clear operational plans, identifying the decision at stake, the evidence needed, and how insights will translate into action.
- Lead strategic deep dives across international expansion, monetization, product penetration, customer segmentation, field deployment, ecosystem dynamics, and organizational design.
- Analyze product usage, customer behavior, adoption patterns, sales and field activity, pipeline and revenue signals, and market context to identify the drivers of B2B growth.
- Partner closely with GTM, Revenue, Product, Finance, RevOps, Strategy, and Data Science leaders to turn analysis into prioritization decisions, field plays, investment choices, and operating cadences.
- Communicate complex findings to executives and cross-functional partners with crisp recommendations, transparent assumptions, and clear tradeoffs.
- Bring practical AI fluency to the work, using modern AI tools to accelerate analysis, synthesize evidence, improve workflows, and identify new ways data science can support business decision-making.
- Help define the operating model for an emerging B2B data science capability, including how the team frames problems, engages stakeholders, evaluates opportunities, and turns insights into GTM action.
You might thrive in this role if you have:
- Senior-level experience in a quantitative role such as Data Science, Product Analytics, Decision Science, Growth Analytics, Strategy Analytics, or a related field.
- Strong business judgment and a track record of translating ambiguous analytical work into decisions that business leaders can act on.
- Experience working on B2B,, GTM or commercial strategy, monetization,, product growth, or similarly complex business problems.
- Strong technical fluency with analytical tools such as SQL and Python, or equivalent experience using data to answer open-ended business questions.
- Ability to connect data across product a coherent view of the business.
- Consulting-style problem solving: you can structure a broad question, identify the highest-leverage analysis, manage uncertainty, and communicate a practical recommendation.
- Excellent executive communication and cross-functional partnership. You can influence senior leaders, field teams, product partners, operators, and other data scientists with clear evidence and balanced judgment.
- Comfort operating in a high-ambiguity, fast-changing environment where the team, roadmap, and stakeholder model are still taking shape.
- Practical AI fluency and curiosity about how modern AI tools can improve analytical workflows, strategic synthesis, and decision support.
- Experience in strategy consulting followed by data science work in technology, or data science work within a consulting analytics practice, is a strong plus.
- An advanced degree is welcome but not required. We care most about your ability to combine analytical rigor, business judgment, and action-oriented communication.
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
$347K – $445K + 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 $347K-$445K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% 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 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($396K) sits 108% above the category median. Disclosed range: $347K to $445K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
OpenAI AI Hiring
OpenAI has 32 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer, Data Scientist, AI Engineering Manager. Positions span New York, NY, San Francisco, CA, US, San Francisco, CA. Compensation range: $280K - $536K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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