Data Scientist, Meta Superintelligence Labs (Safety)

Meta; Menlo Park (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Meta seeks Data Scientists to join the Safety team within MSL (Meta Superintelligence Labs). You will build analytical foundations to advance personal superintelligence safely and securely, turning ambiguous risks into measurable systems across model evaluations and online monitoring.

Work with engineering, research, product, policy, and legal to design measurement and mitigation strategies. You will quantify trade-offs between user friction and safety risks, help operationalize harm prevalence,

Qualifications

• Bachelor's or equivalent in CS/CE or quantitative field.
• 6+ years analytics experience; 4+ years with a PhD.
• Proficient with SQL, Python, or R.
• Experience with frontier AI products or risks in Trust & Safety or Fraud/Risk domains.
• Ability to work in fast-paced, cross-functional environments.
• Strong background in quantitative/statistical methods.
• Master's or PhD in quantitative field.
• Commitment to responsible, ethical AI practices.
• Experience integrating AI tools to improve workflows.
• Ongoing AI skill development (prompt/context engineering).

Skills

SQL

Python

R

Threat modelling

Causal inference

Cross-functional collaboration

Ethical AI practices

Agent orchestration

Prompt/context engineering

Education

Bachelor's degree in CS/CE or quantitative field

Master's or Ph.D. in a quantitative field

Tools

SQL (querying)

Python

R

AI measurement tools

Job description

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.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experienceBachelor's degree in Mathematics, Statistics, Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• A minimum of 6 years of work experience in analytics (minimum of 4 years with a Ph.D.)
• Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)
• 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
• Familiar with 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
• Background in ambiguous and sparse data environments to operationalize e.g. harm prevalence measurement, causal inference, root-cause analysis– rooted in strong quantitative/statistical foundations
• Master's or Ph.D. Degree in a quantitative field
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies