Interested in this Data Scientist role at OpenAI?
Apply Now →Skills & Technologies
About This Role
Careers
Data Scientist, GTM Growth Products
Data Science \- San Francisco
About the role
We are hiring a Data Scientist to support GTM Growth Products, a cross\-functional team building the internal systems that turn OpenAI's technology into measurable revenue leverage.
Our mandate is to prove what an AI\-native revenue organization can become: one where every signal, workflow, and customer moment can be understood, acted on, and improved by agents and humans together. We build systems that help GTM scale faster than headcount by finding opportunity, automating low\-complexity work, routing judgment\-heavy work to the right human, and learning from every outcome.
The team is focused on live GTM workflows across agentic sales, prospecting, routing, workflows, sales policy operations, measurement, attribution, and continuous learning loops.
In this role, you will be embedded with Product, Engineering, and GTM partners to ensure these systems drive measurable business outcomes: ARR, pipeline, conversion, automation, and operating efficiency.
What You’ll Do
Define north\-star, leading, and guardrail metrics for agentic revenue systems, including agentic ARR, incremental pipeline, conversion lift, automation rate, response time, hours saved, and operating efficiency.
Design measurement and experimentation frameworks for always\-on GTM systems, using holdouts, staged rollouts, quasi\-experimental methods, and launch\-specific decision criteria where traditional A/B testing is not enough.
Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value.
Translate messy account, person, product, behavioral, and model\-driven signals into decisions about what to automate, what to route, where humans should intervene, and what the system should learn next.
Build repeatable decision loops from pre\-launch criteria to post\-launch readout to shipped product, policy, or operational changes.
Develop dashboards, readouts, and evaluation systems that help GTM leaders, DS, PM, and engineering teams understand where automation is creating leverage and where quality, policy, or workflow design needs to improve.
Work across GTM Growth, RevOps, Data, Sales, and partnered Engineering teams to connect product changes to business outcomes.
What We’re Looking For
10\+ years in a quantitative role such as Data Science, Product Analytics, Decision Science, or Applied Statistics, ideally at a product\-led company supporting B2B growth, revenue systems, sales, lifecycle, or scaled self\-serve motions.
Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real\-world, always\-on environments.
Strong technical fluency in SQL and Python, including working directly with messy, incomplete operational and behavioral data to extract signal and quantify impact.
Proven track record translating analysis into shipped decisions, not just readouts: changes to product, routing, targeting, automation, policy, workflow design, or GTM strategy.
Strong business judgment and a bias toward action. You can scope ambiguous problems, define success, choose the highest\-leverage measurement path, and move quickly from insight to decision.
Systems thinking and technical maturity. You can reason about agents, workflows, instrumentation, data quality, human review, feedback loops, and operational constraints together.
Excellent communication and cross\-functional partnership. You can influence PMs, engineers, GTM operators, DS peers, and senior leaders with clear recommendations and practical tradeoffs.
Nice to Have
Experience with LLMs, AI agents, agent evaluation, or AI\-assisted operations platforms.
Experience with sales, prospecting, RevOps, lifecycle, support, routing, prioritization, or operational automation systems.
Experience building measurement, experimentation, observability, or attribution frameworks from scratch in early\-stage or rapidly evolving environments.
Familiarity with B2B, SMB, SDR, sales\-assist, or marketplace\-style GTM motions.
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, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see
OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement
.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US\-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non\-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
To notify OpenAI that you believe this job posting is non\-compliant, please submit a report through
this form
. No response will be provided to inquiries unrelated to job posting compliance.
We are committed to providing reasonable accommodations to applicants with disabilities.
OpenAI Global Applicant Privacy Policy
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
$293K – $325K \+ Offers Equity
Salary Context
This $293K-$325K range is above the 75th percentile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($309K) sits 56% above the category median. Disclosed range: $293K to $325K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
OpenAI AI Hiring
OpenAI has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, Research Engineer. Based in San Francisco, CA, US. Compensation range: $230K - $515K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.