Data Scientist - User Risk Measurement

OpenAI

82d

About The Position

As a Data Scientist on this new function, you will help define how the industry measures complex, adaptive human–AI behavior at scale: You will establish trustworthy baselines for user-level risk, create attribution that links changes to real-world mitigations and events, and deliver concise narratives and metrics that guide safety strategy and product decisions. The work spans end-to-end ownership—from framing the questions to delivering decision-ready outputs with clear quality standards and governance—while collaborating closely across Safety Systems, Data Science, Product, and Policy.

Requirements

• Have 3–6+ years in data science, measurement/causal inference, or risk analytics in high-stakes domains
• Are strong in sampling, inference, uncertainty quantification, and rare-event estimation; comfortable with time-varying metrics
• Write solid Python and SQL; are fluent with data warehouses and productionizing notebooks/pipelines
• Communicate crisply, translating complex estimators into clear actions for executives and cross-functional partners

Nice To Haves

• Experience with Airflow DAGs or other ETL pipelines
• Databricks
• Survival analysis
• Streaming/online detection
• Classifier evaluation/QA
• Privacy reviews/audit trails
• Integrity/fraud/safety experience

Responsibilities

• Define the measurement framework for user-level risk across products and cohorts: scope the questions that matter and align on clear, policy-grounded definitions
• Establish baselines and statistical confidence for core metrics: prevalence, intensity, trends, and cohort dynamics
• Build decision-ready reporting surfaces: executive dashboards, weekly briefs, and launch readouts that translate insights into action
• Clean and organize ambiguous data from disparate sources, with an eye toward building automated pipelines and systems
• Create attribution and change-tracking: connect shifts in user behavior to mitigations, product changes, and external events
• Partner across Safety Systems, Data Science, Integrity, Product, and Policy: ensure one coherent analytics entry point and consistent standards
• Uphold quality, privacy, and governance: document methods, ensure auditability, and maintain durable measurement hygiene
• Monitor signals for emerging risks and anomalies: recommend priorities that reduce harmful usage and improve user safety
• Communicate clearly and concisely: deliver insights and trade-offs to executives and engineering teams in language that drives decisions

What This Job Offers

• Number of Employees: 1,001-5,000 employees