About this role
We're seeking Data Scientists to join the Safety team within MSL (Meta Superintelligence Labs).
As a Data Scientist in Safety, you will establish the analytical foundations that allow us to advance personal superintelligence safely and securely. You will help us turn complex, ambiguous risks with incomplete ground truth into measurable systems across model evaluations and online monitoring. You'll work with engineering, research, product, policy, and legal to design and build measurement and mitigation strategies, quantifying trade-offs between user friction and safety risks.
Responsibilities
Measure abuse: build statistical telemetry and measurement frameworks to detect and monitor policy violations or emergent harms
Scale model evaluation: design a scalable framework adapting to evolving agentic model capabilities and safety/risk landscape, grounded in statistics
Evaluate and optimize safeguards: scale offline and online performance evaluation of our safeguards using human-in-the-loop and active learning
Design experiments and analyses: Conduct controlled experiments and rollouts to evaluate the impact of policy or risk definition changes and safety mitigations
Build intelligence and reporting: build signals, pipelines, dashboards to enable real-time monitoring driving interventions and safety roadmaps
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
12+ years of experience in product data science, applied analytics, or quantitative research, including leading complex analytical workstreams in an individual contributor capacity
12+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python or R), and statistical or mathematical software for large-scale data analysis
Experience developing and applying advanced statistical methods including predictive modeling, causal inference, experimentation design, and time series forecasting in a product context
Experience influencing product strategy and executive decision-making through data-driven narratives, written analytical frameworks, and stakeholder alignment across multiple cross-functional teams
Experience establishing analytical standards, data infrastructure best practices, or measurement frameworks adopted broadly across an organization or business domain Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience defining and scaling self-service analytics platforms or data explorability tools that reduce time-to-insight for non-technical stakeholders
Demonstrated use of AI tools to accelerate analytical workflows, including responsible validation of AI-generated outputs and adherence to ethical AI practices
Background in ambiguous and sparse data environments to operationalize e.g. harm prevalence measurement, causal inference, root-cause analysis– rooted in quantitative and statistical foundations
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience working in fast-paced, high-ambiguity, cross-functional environments – able to jump from agentic trace deep-dives to explaining risk dimensions in plain English to policy stakeholders
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Advanced degree (Master's or PhD) in a quantitative field such as Statistics, Computer Science, Mathematics, Economics, or Operations Research
Experience in frontier AI products or risks, and navigating online, adversarial environments in Trust & Safety or Fraud/Risk/Security domains. Model evals, threat modelling, actor telemetry, human-in-the-loop review systems don’t sound foreign to you