Senior Data Scientist (AI)

Location: Remote - United States

Base salary: $190,800–$267,100 USD per year, plus potential equity and commission depending on the position offered.

Company

Reddit is a large online community platform built around user-created communities, discussions, voting, and content sharing.

What you will do

• Own abuse and enforcement problem areas 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 enforcement gaps.
• Build and evaluate classifiers, scoring systems, decision rules, analytical prototypes, and GenAI-based approaches.
• Define evaluation frameworks covering precision, recall, calibration, coverage, robustness, fairness, user friction, reviewer capacity, and operational costs.
• Partner with Product, Engineering, Safety ML, Policy, Safety Operations, Threat Intelligence, and Safety Insights to productionize systems, manage rollouts, and measure real-world impact.
• Support investigations and incident response, build reusable datasets and monitoring tools, communicate recommendations, mentor junior peers, and track advances in adversarial ML, language models, synthetic data, and human-in-the-loop safety systems.

Requirements

• Ph.D. with 4+ years or Master’s degree with 8+ years of relevant industry experience in Data Science, Applied Science, Economics, Statistics, Computer Science, Operations Research, or a related quantitative field.
• 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 including 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, bias toward action, and drive decisions with Product, Engineering, Operations, and Policy teams.
• Exceptional communication skills for explaining technical concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.

Nice to have

• Experience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain.
• Experience with 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 while 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 reviewer quality and capacity analysis.
• Experience assessing fairness, bias, privacy, and policy-aware tradeoffs in automated decision systems.

Culture & Benefits

• Fully remote work within the United States; employees near offices in San Francisco, Los Angeles, New York City, or Chicago may use them voluntarily.
• Comprehensive healthcare and income replacement programs, including mental health and coaching benefits.
• 401(k) with employer match and global benefits supporting workspace, professional development, and caregiving.
• Family planning support, gender-affirming care, flexible vacation, paid volunteer time, and generous paid parental leave.
• The role may involve sensitive content and participation in an incident-response on-call rotation, including occasional work outside normal hours.

Hiring process

• Some interviews may be recorded, transcribed, and summarized by AI; candidates may opt out before scheduled interviews.
• Personal information, including audio or video recordings, may be collected during interviews to evaluate the application.