Job Description

Entity and Actor Integrity (EAI) is a sub-pillar within Facebook Trust and Safety focused on protecting the integrity of creators, groups, and entities across the Meta ecosystem. The team tackles critical challenges including Creator Compromise, Groups Health, Creator Impersonation, and entity recommendability and monetization eligibility. EAI works cross-functionally across Meta, partnering closely with Facebook product teams, Central Integrity, and Growth to build safer, more trustworthy experiences for billions of users.

As a Data Science Manager on EAI, you will lead a team of data scientists to drive insights that shape integrity strategy and product development. You will use data to understand threat landscapes, quantify the impact of bad actors, identify emerging risks, and inform product decisions that protect creators and communities. You will develop analytical frameworks to measure integrity health, design experiments to test interventions, and build scalable measurement systems.

You will partner with Engineering, Product, Policy, and Research teams to translate complex integrity problems into actionable data-driven solutions. As a leader, you will inspire and grow a high-performing team, promote analytical rigor across the team, and build credibility as a trusted strategic partner to cross-functional leadership.

Responsibilities
Lead analytics initiatives to detect, measure, and mitigate threats related to Creator Compromise, Creator Impersonation, and Groups Health
* Drive end-to-end analytics projects in partnership with Product, Engineering, Policy, and Central Integrity teams to inform integrity strategy and investment decisions
* Inspire, lead, and grow a team of data scientists to fulfill EAI's long-term vision and goals
* Develop deep understanding of bad actor behaviors, abuse patterns, and integrity challenges to identify present and future risks and opportunities
* Build analytical frameworks and metrics to measure entity integrity health, recommendability signals, and monetization eligibility
* Design and analyze experiments to evaluate the effectiveness of integrity interventions and product changes
* Work with large and complex data sets to solve challenging problems using statistical modeling, machine learning, and causal inference approaches
* Partner with Central Integrity and Growth teams to align on cross-Meta integrity priorities and share best practices
* Communicate insights and recommendations to leadership, influencing roadmap prioritization and resource allocation

Qualifications
BS degree in a quantitative discipline (e.g., statistics, operations research, econometrics, computer science, engineering), or BS/MS in a quantitative discipline with equivalent working experience
* 7+ years of experience in applied quantitative analysis, statistical modeling, or machine learning in the experimentation space (or 3+ years with a Ph.D. in a relevant quantitative field), including 2+ years of experience managing analytics teams
* 5+ years of experience in a team leadership role, including 2+ years of experience with people management through layers
* Proven track record of leading high-performing analytics teams
* Experience communicating both in low-level technical details as well as high-level strategies
* Track record of driving product roadmap and execution
* Experience in cross-functional partnership among teams of Engineering, Design, PM, Data Engineering Background in adversarial data science, understanding how bad actors adapt to detection systems
* Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
* Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
* Experience with anomaly detection, graph analysis, or entity resolution techniques
* Experience in Trust and Safety, integrity, fraud detection, or abuse prevention domains
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
* Experience working on creator or community health products
* Familiarity with ML and AI operationalization for integrity use cases, including measurement and performance optimization