Senior Data Scientist, Safety Enforcement
New
R
RedditTrust & Safety
Completely remote friendly within the United StatesFull-TimeSenior
Salary$190,800 — $267,100 USD
Apply NowOpens the employer's application page
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 where data may be incomplete or evolving.
- Expertise in SQL and Python for production-grade, reusable analytical code and 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 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 and evaluating language models for detection, investigations, labeling, reviewer assistance, or workflow automation.
Responsibilities
- Own defined abuse or enforcement problems from problem framing and signal discovery through labeling, model or rule development, thresholding, 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 appropriate 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 needs, launch guardrails, monitoring, alerting, escalation paths, and rollback criteria.
- Translate written policy and product requirements into measurable targets, labels, decision rules, reviewer workflows, and enforcement outcomes.
- Design staged rollouts, experiments, and observational analyses to measure harm reduction, false positives, and downstream effects.
- Support investigations and urgent escalations, identify mitigation options, conduct post-incident analyses, and improve detection and monitoring.
- Build reusable datasets, evaluation harnesses, monitoring tools, and documentation; communicate recommendations and mentor junior peers.
- Track emerging abuse tactics and advances in detection, adversarial ML, language models, synthetic data, and human-in-the-loop safety systems.
View Full Description & ApplyYou'll be redirected to the employer's site
