Senior Data Scientist (Safety Enforcement)

Join Reddit as a Senior Data Scientist in Safety Enforcement. You will own the enforcement lifecycle, analyze large-scale data to identify abuse patterns, develop decision systems, and work with engineering partners to implement solutions. You will also translate policy into systems, measure real-world impact, and scale the team's effectiveness. The ideal candidate will have a Ph.D. or Master's degree with relevant industry experience, exceptional communication skills, and a strong background in statistical analysis and machine learning techniques.

• Lead the enforcement lifecycle for defined abuse or enforcement problems, from problem framing to rollout and monitoring.
• Analyze large-scale data to identify adversarial patterns and emerging threats, and develop models and classifiers to address these issues.
• Collaborate with engineering partners to productionize promising approaches, define data and logging needs, and launch monitoring and alerting systems.

• Education & Experience: Ph.D. with 4+ years OR Master’s degree with 8+ years* of relevant industry experience in Data Science, Applied Science, Economics, or a related quantitative role (e.g., Statistics, Computer Science, Operations Research)
• Proven ability to deconstruct ambiguous problems into scopable components, bias toward action, and drive impactful decisions alongside Product, Engineering, Operations, and Policy teams
• Exceptional communication skills with demonstrated ability to convey complex technical concepts, tradeoffs, and recommendations to both technical and non-technical stakeholders
• Applied experience in safety, enforcement engineering, fraud, or similar fast-paced, adversarial domains where data may be incomplete or evolving
• Practical knowledge of evaluating real-world decision systems, including sampling, label quality, class imbalance, precision/recall, calibration, thresholding, drift, and monitoring
• Expertise in SQL and Python to write production-grade, reusable analytical code and contribute directly to data pipelines
• Strong background in statistical analysis, experiment design, causal modeling, and ML techniques (e.g., classification, ranking, anomaly/pattern detection)
• Experience with one or more enforcement-relevant methods such as graph or network analysis, NLP, embeddings, sequence or behavioral modeling, weak supervision, synthetic data, or human-in-the-loop evaluation
• Experience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain
• Experience applying language models to detection, investigations, labeling, reviewer assistance, or workflow automation—and measuring quality, cost, latency, and safety tradeoffs
• Experience partnering with engineers to launch and monitor models or rules in production, 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