Senior Data Scientist, Safety Enforcement
New
R
Reddit, Inc.Trust & Safety
Remote - United States; completely remote friendly within the United StatesFull-TimeSenior
Salary$190,800 — $267,100 USD
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Job Details
- Experience
- Ph.D. with 4+ years OR Master’s degree with 8+ years of relevant industry experience
- Required Skills
- PythonSQLMachine Learning
Requirements
- 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.
- Applied experience in safety, enforcement engineering, fraud, or similar fast-paced, adversarial domains.
- Expertise in SQL and Python for production-grade, reusable analytical code and data-pipeline contributions.
- 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 break ambiguous problems into scoped components and drive decisions with Product, Engineering, Operations, and Policy teams.
- Ability to communicate technical concepts, tradeoffs, and recommendations 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.
- Preferred: experience with graph or network analysis, NLP, embeddings, sequence or behavioral modeling, weak supervision, synthetic data, or human-in-the-loop evaluation.
- Preferred: experience applying language models to detection, investigations, labeling, reviewer assistance, or workflow automation and evaluating quality, cost, latency, and safety tradeoffs.
- Preferred: experience launching and monitoring production models or rules, designing human-review prioritization systems, or assessing fairness, bias, privacy, and policy-aware tradeoffs.
Responsibilities
- Own defined abuse or enforcement problems from framing and signal discovery through labels, model or rule development, rollout, monitoring, and iteration.
- Analyze behavioral, content, account, and network data to identify adversarial patterns, emerging threats, and mitigation gaps.
- Develop and evaluate models, classifiers, scoring systems, rules, and analytical prototypes using statistical, machine-learning, or GenAI methods.
- Create evaluation frameworks covering precision, recall, calibration, coverage, robustness, fairness, user friction, reviewer capacity, and operational costs.
- Partner with Engineering and Safety ML to productionize approaches and define data, logging, launch guardrails, monitoring, alerting, escalation, and rollback criteria.
- Work with Policy and Safety Operations to translate written policy into targets, labels, decision rules, reviewer workflows, and measurable outcomes.
- Design rollouts, experiments, and observational analyses to test interventions and measure effects on users, communities, and operations.
- Support investigations and urgent escalations, conduct post-incident analyses, and develop durable detection and monitoring improvements.
- Build reusable datasets, evaluation harnesses, monitoring tools, and documentation; communicate recommendations and mentor junior peers.
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