Trust & Safety Data Scientist — Detect & Defuse Abuse

Replit; California (MO); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Competitive salary & equity

401(k) match

Health, dental, vision & life insure.

Disability insurance

Parental leave

Flexible time off

Commuter benefits

Wellness stipend

In-office setup reimbursement

Quarterly team gatherings

In-office amenities

Job summary

Replit is seeking a data scientist to own the analytical foundation for Trust & Safety, building models and dashboards to measure abuse, fraud loss, and detection performance.

You will develop risk rules, offline evaluations, and scalable data pipelines, partnering with product, engineering, and legal to translate signals into actionable playbooks. This full-time role requires in-office presence in Foster City, CA, with Monday, Wednesday and Friday in the office.

Qualifications

• 5+ years 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.

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.

Skills

SQL

Python

Data Modeling

Big Data

Tools

dbt

BigQuery

Snowflake

Amplitude

Job description

Replit is seeking a data scientist to own the analytical foundation for Trust & Safety, building models and dashboards to measure abuse, fraud loss, and detection performance.

You will develop risk rules, offline evaluations, and scalable data pipelines, partnering with product, engineering, and legal to translate signals into actionable playbooks. This full-time role requires in-office presence in Foster City, CA, with Monday, Wednesday and Friday in the office.