Senior Data Scientist, Safety Enforcement

Remote; San Francisco (CA); Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Comprehensive Healthcare Benefits and

401k with Employer Match

Global Benefit programs

Family Planning Support

Mental Health & Coaching Benefits

Job summary

Reddit is seeking a Senior Data Scientist to join Safety Enforcement Data Science. You will own enforcement problem areas, translate policy into measurable signals, labels, and evaluation plans, and collaborate with Product, Engineering, ML, Policy, and Safety Operations to scale mitigation.

This hands-on role involves prototyping models, tuning thresholds, and designing rigorous evaluation with human oversight. Remote-friendly within the United States, with potential office access as needed.

Qualifications

• Ph.D. with 4+ years OR Master’s with 8+ years in Data Science, Applied Science, Economics, or related field.
• Applied experience in safety, enforcement engineering, fraud, or similar adversarial domains.
• Expertise in SQL and Python to write production‑grade, reusable analytical code and data pipelines.
• Strong background in statistical analysis, experiment design, causal modeling, and ML techniques.
• Experience evaluating real‑world decision systems and monitoring.
• Ability to deconstruct ambiguous problems and drive decisions with cross‑functional teams.
• Exceptional communication skills for technical and non‑technical stakeholders.

Responsibilities

• Own the enforcement lifecycle from problem framing to rollout and monitoring.
• Discover and characterize abuse by analyzing large-scale data.
• Build and evaluate decision systems using ML or rules.
• Define evaluation standards: precision, recall, calibration, fairness, and costs.
• Ship with engineering partners to productionize approaches.
• Translate policy into modelable targets and reviewer workflows.
• Measure real-world impact with staged rollouts and experiments.
• Respond to incidents and drive post‑incident improvements.
• Scale the team’s datasets, tooling, and documentation.
• Stay ahead of threat landscape with advanced methods.

Skills

Statistics

Experiment design

Causal modeling

ML techniques

Communication

Education

Ph.D. with 4+ years OR Master’s with 8+ years in Data Science/related field

Tools

SQL

Python

Job description

Defining the future of work & living globally

5,814 jobs · 6 companies

Senior Data Scientist, Safety Enforcement

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .

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.

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Mental Health & Coaching Benefits