Senior Data Scientist, Safety Enforcement

Details

Reddit is seeking a Senior Data Scientist to join the Safety Enforcement Data Science team. The role focuses on translating ambiguous abuse risks and written policy into measurable signals, labels, detection approaches, actioning logic, and evaluation plans. You will work with Product, Engineering, Machine Learning, Policy, Safety Operations, Threat Intelligence, and Safety Insights teams to develop and scale systems that detect and mitigate abuse effectively, efficiently, and fairly.

This is a hands-on role involving adversarial behavior investigation, model and rule prototyping, decision-threshold tuning, launch-guardrail definition, enforcement-gap diagnosis, and post-deployment monitoring. The role may involve exposure to sensitive content and participation in an incident-response on-call rotation, including occasional work outside normal hours.

Responsibilities

• Own the enforcement lifecycle for defined abuse or enforcement problems, from problem framing and signal discovery through labeling, taxonomy development, model or rule development, thresholding, rollout, monitoring, and iteration.
• Analyze large-scale behavioral, content, account, and network data to identify adversarial patterns, emerging threats, and gaps in existing mitigations.
• Develop models, classifiers, scoring systems, rules, and analytical prototypes using statistical, machine-learning, or GenAI methods.
• Create evaluation frameworks addressing precision, recall, calibration, coverage, robustness, fairness, user friction, reviewer capacity, and other operational costs.
• Partner with Engineering and Safety ML to productionize approaches, define data and logging requirements, launch guardrails, monitoring and alerting, escalation paths, and rollback criteria.
• Translate written policy and product requirements into modelable targets, labels, decision rules, reviewer workflows, and measurable enforcement outcomes.
• Design staged rollouts, experiments, and observational analyses to evaluate whether interventions reduce harm while managing false positives and downstream effects.
• Support investigations and urgent escalations, identify mitigation options, conduct post-incident analyses, and develop durable detection or monitoring improvements.
• Build reusable datasets, evaluation harnesses, monitoring tools, and documentation; communicate recommendations to technical and non-technical partners; and mentor junior peers.
• Track emerging abuse tactics and advances in detection, adversarial machine learning, language models, synthetic data, and human-in-the-loop safety systems.

Requirements

• Ph.D. with 4+ years or a Master's degree with 8+ years of relevant industry experience in Data Science, Applied Science, Economics, or a related quantitative field such as Statistics, Computer Science, or Operations Research.
• Applied experience in safety, enforcement engineering, fraud, or similar fast-paced, adversarial domains where data may be incomplete or evolving.
• Expertise in SQL and Python, including production-grade, reusable analytical code and direct contributions to data pipelines.
• Strong background in statistical analysis, experiment design, causal modeling, and machine-learning techniques such as classification, ranking, and anomaly or pattern detection.
• Practical knowledge of evaluating real-world decision systems, including sampling, label quality, class imbalance, precision and recall, calibration, thresholding, drift, and monitoring.
• Ability to deconstruct ambiguous problems into actionable components and drive decisions with Product, Engineering, Operations, and Policy teams.
• Exceptional communication skills, including the ability to explain complex technical concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.

Preferred Qualifications

• Experience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain.
• Experience with graph or network analysis, NLP, embeddings, sequence or behavioral modeling, weak supervision, synthetic data, or human-in-the-loop evaluation.
• Experience applying language models to detection, investigations, labeling, reviewer assistance, or workflow automation, including measurement of quality, cost, latency, and safety tradeoffs.
• Experience partnering with engineers to launch and monitor production models or rules, including incident response and post-launch tuning.
• Experience designing prioritization or triage systems for human review operations, including analysis of reviewer quality and capacity.
• Experience assessing fairness, bias, privacy, and policy-aware tradeoffs in automated decision systems.

Benefits

• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefit programs supporting workspace, professional development, and caregiving.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.
• Eligibility for equity in the form of restricted stock units.

Compensation

The base salary range for this position is $190,800–$267,100 USD. The role may also be eligible for equity and, depending on the position offered, commission. Final offer amounts may vary based on skills, experience, and relevant credentials.