Data Scientist

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.

We're hiring a Data Scientist to help build Replit's Trust & Safety and Anti-Abuse program from the ground up. You'll turn noisy behavioral, identity, payment, infrastructure, and content signals into the measurement systems, detections, and decisions that protect Replit's users, platform, and economics. You'll work closely with Engineering, Support, Legal, Security, Infrastructure, Money, and Growth to make abuse economically unviable while keeping friction low for legitimate users.

• Own the analytical foundation for Trust & Safety, including abuse prevalence, fraud loss, false-positive and false-negative rates, time to detect, time to mitigate, appeal and reversal rates, and verification step-up conversion.
• Build reliable datasets and dbt models that connect product events, account and identity signals, payment activity, infrastructure usage, content classifications, enforcement actions, appeals, and support outcomes.
• Develop and evaluate risk models, rules, and anomaly-detection systems for threats such as phishing, scam hosting, cryptomining, token farming, payment fraud, promotional abuse, and AI-agent exploitation.
• Design rigorous offline evaluations, shadow-mode tests, holdouts, and controlled experiments to measure detection quality and the user impact of new policies, enforcement actions, and progressive verification.
• Define thresholds and decision frameworks that balance abuse reduction, economic loss, customer friction, and false positives across free, paid, and enterprise users.
• Investigate emerging abuse patterns, quantify their impact, identify coordinated behavior, and turn ambiguous signals into clear recommendations for product and engineering teams.
• Develop predictive models that estimate account, device, transaction, workspace, or deployment risk and embed those signals into detection, review, and escalation workflows.
• Partner with Support and Legal to improve case review, appeals, reason-code quality, and feedback loops so human decisions become useful model and policy signals.
• Build monitoring that detects model drift, attacker adaptation, data-quality failures, and unexpected harm to legitimate users.
• Communicate findings clearly to technical and non-technical partners, including the tradeoffs, uncertainty, and evidence behind high-impact decisions.

• 5+ years of experience in data science, product analytics, fraud, risk, trust and safety, or a related field.
• Strong SQL and Python skills, with experience working with large behavioral datasets and building reliable data models or pipelines.
• Experience developing and evaluating predictive models, experiments, or decision systems, with sound judgment around uncertainty and tradeoffs.
• Ability to turn ambiguous data into clear recommendations and communicate them effectively across technical and non-technical teams.
• Comfort working with imperfect labels, biased samples, and high-impact decisions where false positives matter.
• You use AI tools extensively to increase your effectiveness while maintaining a high bar for analytical quality.

• Experience building or evaluating anti-abuse, fraud, identity, security, spam, integrity, or content-safety systems at scale.
• Built, shipped, and maintained ML models in production (classification, anomaly detection, or risk scoring), including feature engineering on behavioral and transaction data, threshold selection against precision/recall economics, and post-launch monitoring.
• Experience with graph analysis, entity resolution, coordinated-behavior detection, reputation systems, anomaly detection, or risk scoring.
• Experience measuring false positives and enforcement harm, designing human-review workflows, or using appeals and case outcomes as model feedback.
• Familiarity with progressive verification, KYC, account trust, or identity providers such as Prove, Persona, Socure, or Stripe Identity.
• Experience with causal inference methods such as difference-in-differences, propensity score methods, synthetic control, or uplift modeling.
• Experience with a modern data stack such as dbt, BigQuery, Snowflake, Fivetran, Amplitude, Mixpanel, or Segment.
• Experience at a consumer platform, developer tool, cloud provider, marketplace, fintech company, or other product with a meaningful adversarial surface.

Base salary for this role ranges from $210K – $310K, plus equity. Full-time employee benefits include: 401(k) program with a 4% match (US only), health, dental, vision and life insurance, short-term and long-term disability, paid parental, medical, caregiver leave, flexible time off (FTO) plus holidays, commuter benefits (in-office & US only), monthly wellness stipend, autonomous work environment, in-office setup reimbursement (in-office only), quarterly team gatherings, and in-office amenities (in-office only).

This is a full-time role that can be held from our Foster City, CA office. The role has an in-office requirement of Monday, Wednesday, and Friday.