Senior Data Scientist, Safety Enforcement

Reddit is continuing to grow our teams with the best talent. This role is completely remote friendly within the United States. If you happen to live close to one of our physical office locations (San Francisco, Los Angeles, New York City & Chicago) our doors are open for you to come into the office as often as you'd like.

Safety is central to Reddit’s mission and long-term health. Safety & Mods Data Science operates as a closed-loop system: Safety Insights helps the organization understand, measure, and prioritize risk, while Safety Enforcement Data Science builds and optimizes the models, rules, and systems that detect and mitigate abuse effectively, efficiently, and fairly.

We are looking for a Senior Data Scientist to join Safety Enforcement Data Science. You will own one or more defined enforcement problem areas and translate ambiguous abuse risks and written policy into measurable signals, labels, detection approaches, actioning logic, and evaluation plans. You will work closely with Product, Engineering, Machine Learning, Policy, Safety Operations, Threat Intelligence, and Safety Insights partners to turn analysis into scaled mitigation.

This is a hands-on role. You will investigate adversarial behavior, prototype and evaluate models and rules, tune decision thresholds, define launch guardrails, diagnose enforcement gaps, and monitor systems after deployment. You will also help the team use modern AI methods—including language models—where they improve detection, investigations, labeling, or operational workflows, with rigorous evaluation and appropriate human oversight.

This 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. Lead a defined abuse or enforcement problem from problem framing and signal discovery through labels and taxonomies, model or rule development, thresholding, rollout, monitoring, and iteration.
• Discover and characterize abuse. Analyze large-scale behavioral, content, account, and network data to identify adversarial patterns, emerging threats, and gaps in existing mitigations.
• Build and evaluate decision systems. Develop models, classifiers, scoring systems, rules, and analytical prototypes using the statistical, machine-learning, or GenAI methods best suited to the problem.
• Define decision-quality standards. Create evaluation frameworks that address precision, recall, calibration, coverage, robustness, fairness, user friction, reviewer capacity, and other operational costs—not model performance in isolation.
• Ship with engineering partners. Partner with Engineering and Safety ML to productionize promising approaches; define data and logging needs, launch guardrails, monitoring and alerting, escalation paths, and rollback criteria.
• Translate policy into systems. Work with Policy and Safety Operations to convert written policy and product requirements into modelable targets, labels, decision rules, reviewer workflows, and measurable enforcement outcomes.
• Measure real-world impact. Design staged rollouts, experiments, and observational analyses that test whether interventions reduce harm while managing false positives and downstream effects on users, communities, and operations.
• Respond and learn. Support investigations and urgent escalations, identify mitigation options, conduct post-incident analyses, and turn one-time findings into durable detection or monitoring improvements.
• Scale the team’s effectiveness. Build reusable datasets, evaluation harnesses, monitoring tools, and documentation; communicate clear recommendations to technical and non-technical partners; and mentor junior peers when opportunities arise.
• Stay ahead of the threat landscape. Track emerging abuse tactics and advances in detection, adversarial ML, language models, synthetic data, and human-in-the-loop safety systems, and apply them pragmatically.

Required Qualifications:

• 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).
• 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 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).
• Practical knowledge of evaluating real-world decision systems, including sampling, label quality, class imbalance, precision/recall, calibration, thresholding, drift, and monitoring.
• 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.

Preferred qualifications

• Experience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain.
• 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 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.

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