Data Science Manager, Entity and Actor Integrity (EAI) Job at Meta (Menlo Park)

Job Responsibility

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

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