Data Scientist, Strategic Intelligence & Risk
OpenAI
Job Overview
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Job Description
About The Team
The Intelligence and Investigations team at OpenAI focuses on rapidly identifying and mitigating abuse and strategic risks to ensure a safe online ecosystem. They are dedicated to understanding emerging abuse trends, analyzing risks, and collaborating with internal and external partners to implement effective mitigation strategies. This team's work is crucial in supporting OpenAI's mission to develop AI that benefits humanity.
The Strategic Intelligence & Analysis (SIA) team specifically provides safety intelligence for OpenAI’s products. This involves monitoring, analyzing, and forecasting real-world abuse, geopolitical risks, and strategic threats. Their insights inform safety mitigations, product decisions, and partnerships, ensuring the secure and responsible deployment of OpenAI’s tools across critical sectors.
About The Role
As a Data Scientist, Strategic Intelligence & Risk, you will be instrumental in leading econometric and experimental analyses to comprehend how risk evolves within complex human–AI systems. Your primary focus will be on quantifying the magnitude and impact of risk shifts in a dynamic operational environment. This involves designing experiments and observational studies to pinpoint causal drivers and analyze risk changes across various surfaces and sources. Your analyses will directly influence prioritization and strategic risk management throughout the company.
This hybrid role is based in San Francisco, CA, requiring 3 days/week in the office. Relocation support is available.
Key Responsibilities
- Own the design and execution of experimental and observational analyses to assess strategic risk effectively.
- Develop robust econometric approaches to estimate the impact of product, policy, and external developments on key risk vectors.
- Translate complex strategic risk questions into testable hypotheses and well-defined study designs.
- Design and deploy A/B tests and pseudo-experimental studies to measure risk changes and understand underlying mechanisms.
- Identify, test, and explain product-driven, event-driven, or signal-driven changes in risk.
- Establish baselines and statistical confidence for core metrics to accurately size potential problems.
- Collaborate with cross-functional teams to track strategic risks, identify intervention opportunities, and develop analyses to evaluate these interventions.
What You'll Bring
- 3–6+ years of experience in econometrics, causal inference, or experimental research.
- Comfort in owning ambiguous analyses with significant company-wide influence.
- Strong expertise in experimental design, observational methods, and statistical reasoning.
- Proficiency in Python and SQL for data analysis and modeling.
- Proven experience in delivering zero-to-one analyses and scaling them from concept through deployment.
- Ability to communicate complex data-driven findings clearly, including uncertainties and trade-offs, to both technical and non-technical partners and leadership.
- Nice to have: experience in trust and safety, integrity, operational security, intelligence analysis, or other quantitative risk-focused domains.
Key skills/competency
- Econometrics
- Causal Inference
- Experimental Design
- Statistical Reasoning
- Python
- SQL
- Risk Management
- Strategic Analysis
- Data Modeling
- Impact Assessment
How to Get Hired at OpenAI
- Research OpenAI's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor. Focus on their dedication to AI safety and beneficial deployment.
- Tailor your resume: Customize your resume to highlight experience in econometrics, causal inference, Python, SQL, and strategic risk analysis. Quantify your impact on past projects.
- Showcase problem-solving skills: Prepare to demonstrate your ability to tackle ambiguous, large-scale analytical challenges through case studies or project examples.
- Ace the technical interview: Brush up on advanced statistical methods, experimental design, and your proficiency in Python and SQL, especially for data manipulation and causal modeling.
- Communicate clearly: Practice explaining complex data findings, uncertainties, and trade-offs to non-technical stakeholders, emphasizing how your insights drive strategic decisions.
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