Senior Data Scientist, Safety Enforcement

Role Overview

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.

*Note: 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.
• 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.
• Respond and learn: Support investigations and urgent escalations, identify mitigation options, conduct post-incident analyses, and turn one-time findings into durable improvements.
• Scale the team’s effectiveness: Build reusable datasets, evaluation harnesses, monitoring tools, and documentation; 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.

Qualifications

Required

• 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 cross-functional teams.
• Exceptional communication skills with demonstrated ability to convey complex technical concepts to technical and non-technical stakeholders.

Preferred

• Experience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain.
• Experience with 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.
• Experience assessing fairness, bias, privacy, and policy-aware tradeoffs in automated decision systems.

Interview Process Notice

In select roles and locations, interviews will be recorded, transcribed, and summarized by artificial intelligence (AI). Candidates have the opportunity to opt out prior to any scheduled interviews.