Senior Data Scientist, Safety Enforcement

1d•$190,800 - $267,100•Remote

About The Position

Reddit is seeking a Senior Data Scientist to join Safety Enforcement Data Science. The role owns one or more defined enforcement problem areas, translating ambiguous abuse risks and written policy into measurable signals, labels, detection approaches, actioning logic, and evaluation plans. The position works cross-functionally with Product, Engineering, Machine Learning, Policy, Safety Operations, Threat Intelligence, and Safety Insights to turn analysis into scaled mitigation. This is a hands-on role involving investigation of adversarial behavior, prototyping and evaluating models and rules, tuning decision thresholds, defining launch guardrails, diagnosing enforcement gaps, and monitoring deployed systems. The scientist will also help apply modern AI methods, including language models, when they improve detection, investigations, labeling, or operational workflows, using rigorous evaluation and appropriate human oversight. The role may involve exposure to sensitive content and participation in an incident-response on-call rotation, including occasional work outside normal hours. The role is completely remote-friendly within the United States. Employees located near Reddit offices in San Francisco, Los Angeles, New York City, or Chicago may work from an office as often as they like.

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 role 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 for 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 scopable components, bias toward action, and drive impactful decisions with Product, Engineering, Operations, and Policy teams.
Exceptional communication skills, including the ability to explain technical concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.

Nice To Haves

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 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.

Responsibilities

Own the enforcement lifecycle for 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.
Analyze behavioral, content, account, and network data to identify adversarial patterns, emerging threats, and gaps in existing mitigations.
Develop and evaluate models, classifiers, scoring systems, rules, and analytical prototypes using suitable statistical, machine-learning, or GenAI methods.
Create evaluation frameworks covering precision, recall, calibration, coverage, robustness, fairness, user friction, reviewer capacity, and other operational costs.
Partner with Engineering and Safety ML to productionize approaches, and define data and logging needs, launch guardrails, monitoring and alerting, escalation paths, and rollback criteria.
Work with Policy and Safety Operations to 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 measure whether interventions reduce harm while managing false positives and downstream effects on users, communities, and operations.
Support investigations and urgent escalations, identify mitigation options, conduct post-incident analyses, and turn findings into 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 when opportunities arise.
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.

Benefits

Comprehensive healthcare benefits and income replacement programs
401(k) with employer match
Global benefit programs including workspace, professional development, and caregiving support
Family planning support
Gender-affirming care
Mental health and coaching benefits
Flexible vacation
Paid volunteer time off
Paid parental leave
Medical, dental, and vision insurance
Equity in the form of restricted stock units; commission may also apply depending on the position offered