数据科学家

Meta（Meta Platforms）岗位：Data Scientist, Meta Superintelligence Labs (Safety) 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

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

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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 indi