# Data Scientist, Trust & Safety

**Company:** [Replit](https://hotfix.jobs/companies/replit)
**Location:** Foster City, CA
**Role:** Data Science
**Salary:** $210k – $310k/yr
**Experience:** 5+ years
**Skills:** SQL, Python, dbt, BigQuery, Snowflake, Machine Learning, Anomaly Detection, graph analysis, Causal Inference, A/B Testing
**Posted:** 2026-07-20

> Data Scientist building measurement systems, risk models, and experiments to detect and mitigate AI-native abuse, fraud, and adversarial behavior on Replit's platform while minimizing impact on legitimate users. Requires 5+ years in data science or fraud/risk, strong SQL/Python, and experience with imperfect labels and high-stakes decisions.

## Job Description

## Responsibilities
- 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.

## Required Skills and Experience
- 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.
- Use AI tools extensively to increase effectiveness while maintaining a high bar for analytical quality.

## Preferred Qualifications
- 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.

## Bonus Points
- Built AI-powered analytical tools, investigation systems, automated detections, or novel measurement approaches.
- Experience with AI-native abuse such as prompt injection, LLM token farming, model extraction, or agent-driven abuse.
- Understand freemium, usage-based, or promotional pricing models and the abuse vectors they create.

## Similar roles

- [Data Scientist, Finance Forecasting](https://hotfix.jobs/jobs/87758d2a-57c5-40fb-bcdc-6b42f6a9c38b) - Clickhouse - Menlo Park, CA - $215k – $267k/yr
- [Data Scientist](https://hotfix.jobs/jobs/4caf384d-b7a7-4738-9279-e42cb2461742) - Parafin - San Francisco, CA - $200k – $330k/yr
- [Research Scientist (Measurement and Evaluation)](https://hotfix.jobs/jobs/ef089b22-6fd3-40cc-bfa9-73082a3b233e) - Abridge - New York, NY - $220k – $280k/yr
- [Quantitative Intelligence Analyst](https://hotfix.jobs/jobs/6cbbee7d-f875-436e-b027-7552bf005b53) - OpenAI - San Francisco, CA - $198k – $320k/yr
- [Data Scientist](https://hotfix.jobs/jobs/67fefd92-51a8-433a-8552-e0ba722b32ba) - Agentio - New York, NY - $195k – $350k/yr

**Apply:** https://hotfix.jobs/jobs/50bea744-a4c4-489b-bac3-9480fa9f54a6
**Canonical:** https://hotfix.jobs/jobs/50bea744-a4c4-489b-bac3-9480fa9f54a6