{"id":3049896,"url":"https://alion.io/job/reddit-senior-data-scientist-safety-enforcement","title":"Senior Data Scientist, Safety Enforcement","company":{"id":16,"name":"Reddit","domain":"reddit.com","url":"https://alion.io/company/reddit","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":79,"open_postings":26,"ghost_share":0,"stale_share":0.654,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":190800,"max":267100,"currency":"USD","period":"year","gross":null,"usd_annual":267100},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Human-in-the-Loop","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Embeddings","optional":true},{"name":"NLP","optional":true}],"status":"live","first_seen_at":"2026-10-21T23:07:25Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-27T06:44:06Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"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.\nReddit 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.\nSafety 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.\nWe 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.\nThis 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.\nThis role may involve exposure to sensitive content and participation in an incident-response on-call rotation, including occasional work outside normal hours.\nResponsibilities:\nOwn 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.\nDiscover and characterize abuse. Analyze large-scale behavioral, content, account, and network data to identify adversarial patterns, emerging threats, and gaps in existing mitigations.\nBuild 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.\nDefine 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.\nShip 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.\nTranslate 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.\nMeasure 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.\nRespond 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.\nScale 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.\nStay 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.\nRequired Qualifications:\nEducation & 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).\nApplied experience in safety, enforcement engineering, fraud, or similar fast-paced, adversarial domains where data may be incomplete or evolving.\nExpertise in SQL and Python to write production-grade, reusable analytical code and contribute directly to data pipelines.\nStrong background in statistical analysis, experiment design, causal modeling, and ML techniques (e.g., classification, ranking, anomaly/pattern detection).\nPractical knowledge of evaluating real-world decision systems, including sampling, label quality, class imbalance, precision/recall, calibration, thresholding, drift, and monitoring.\nProven ability to deconstruct ambiguous problems into scopable components, bias toward action, and drive impactful decisions alongside Product, Engineering, Operations, and Policy teams.\nExceptional communication skills with demonstrated ability to convey complex technical concepts, tradeoffs, and recommendations to both technical and non-technical stakeholders.\nPreferred qualifications\nExperience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain.\nExperience 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.\nExperience applying language models to detection, investigations, labeling, reviewer assistance, or workflow automation-and measuring quality, cost, latency, and safety tradeoffs.\nExperience partnering with engineers to launch and monitor models or rules in production, including incident response and post-launch tuning.\nExperience designing prioritization or triage systems for human review operations, including analysis of reviewer quality and capacity.\nExperience assessing fairness, bias, privacy, and policy-aware tradeoffs in automated decision systems.\nBenefits:\nComprehensive Healthcare Benefits and Income Replacement Programs\n401k with Employer Match\nGlobal Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\nFamily Planning Support\nGender-Affirming Care\nMental Health & Coaching Benefits\nFlexible Vacation & Paid Volunteer Time Off\nGenerous Paid Parental Leave \nPay Transparency:\nThis job posting may span more than one career level.\nIn addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.\nTo provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.\nThe base salary range for this position is:\n$190,800—$267,100 USD\nIn select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.\nDuring the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.\nReddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. 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