Data Science Manager, Integrity

About the Team

Integrity Data Science sits at the center of OpenAI’s mission to deploy powerful AI responsibly. We help ensure people can trust our products by building measurement systems, experimentation practices, and detection / mitigation strategies that protect OpenAI and our users from misuse, fraud, and evolving adversarial behaviors.

As the scope and urgency of Integrity work expands across product surfaces and go-to-market motion, we’re hiring a dedicated Data Science Manager to scale the team, strengthen execution across multiple Integrity domains, and deepen partnership with Product, Engineering, Operations, and adjacent orgs (e.g., Growth, Ads).

This role is based in our San Francisco HQ (in-office).

About the Role

As Data Science Manager, Integrity, you will lead a team of data scientists working across trust & safety, fraud prevention, risk analysis, measurement, and modeling. You’ll be accountable for building a high-performing DS function that can keep pace with fast-moving threats—and for shaping the analytical strategy that informs how OpenAI detects, measures, and mitigates integrity risks at scale.

This is a highly cross-functional leadership role. You’ll help set the roadmap with Integrity Product / Engineering leaders, evolve team structure and operating rhythms, raise the bar on technical rigor (experimentation, causal inference, modeling, metrics), and develop a culture of proactive, high-leverage impact. Many of the challenges in this space are emergent—new misuse patterns appear as the technology and ecosystem evolves—so this role requires strong judgment, comfort with ambiguity, and an ability to build systems that scale.

In this role, you will :

Lead and scale a high-impact Integrity Data Science team—hiring, coaching, and developing DS ICs (and potentially future managers) while setting a strong technical and cultural bar.

Drive strategy across multiple Integrity domains (policy enforcement, bot detection, fraud prevention, IP theft, risk measurement, abuse prevention), balancing near-term response with durable systems.

Build and institutionalize analytical rigor : clear metric frameworks, experimentation standards, monitoring / alerting, and repeatable evaluation approaches for Integrity interventions.

Partner deeply with Product & Engineering to shape roadmaps, prioritize the right bets, and translate ambiguous risk signals into practical product and platform decisions.

Evolve team structure and operating model as the org scales—defining ownership boundaries, improving processes, and creating leverage through better tooling and AI-assisted workflows.

Enable cross-org outcomes , supporting partners outside Integrity (e.g., Growth, Ads, GTM) where integrity risks intersect with product and business goals.

Communicate clearly with senior leadership , synthesizing complex tradeoffs, surfacing risk, and driving alignment on priorities and success metrics.

Push the team toward an AI-leveraged operating mode , using modern tooling and model capabilities to accelerate detection, triage, analysis, and iteration.

You might thrive in this role if you :

Have deep experience leading and scaling Data Science teams, ideally in trust & safety, fraud / abuse, security, risk, or other adversarial problem spaces in fast-moving environments.

Bring strong technical grounding across modern DS techniques (experimentation, causal inference, anomaly detection, risk modeling, measurement design) and can coach others to execute with rigor.

Have a track record of building durable partnerships across DS, Engineering, Product, and Operations—able to influence without authority and create shared accountability.

Are excellent at hiring, mentoring, and developing technical talent, and can build a culture that is both high-bar and supportive.

Can translate messy, evolving threats into clear frameworks, metrics, and decisions—and keep the team focused on the highest-leverage work.

Are comfortable operating in ambiguity, and can bring structure, clarity, and momentum where the “right answer” isn’t obvious.

Bonus if you :

Have experience deploying scaled detection solutions using LLMs, embeddings, fine-tuning, or related ML systems for abuse / fraud / risk.

Have worked closely with policy, content moderation, investigations, or security operations teams and understand how to design analytics that actually works end-to-end.

Have built or led measurement systems that balance safety, user experience, and operational / business constraints.