Senior Data Scientist - Fraud Prevention

United StatesRemote

Responsibilities

Design and Lead Key Analyses and Metric Evolution
Analyze large, complex datasets to identify abuse patterns, fraud signals, and harmful behavior trends.
Conduct root cause analysis to diagnose safety incidents and emerging risks.
Evaluate new tool effectiveness (including AI), and impact on agent efficiency and user satisfaction.
Define and track core metrics (e.g., harm prevalence, violation rates, detection accuracy).
Build dashboards and reporting frameworks to track platform health and safety performance.
Develop Models & Rules
Develop heuristics, statistical models, and machine learning solutions for proactive detection of abuse, fraud, or harmful content
Build prediction systems (e.g., anomaly detection, risk scoring, behavioral profiling).
Improve automated enforcement and moderation workflows.
Evaluate model performance and iterate on detection strategies.
Evaluate Product / Policy Changes Via Experimentation
Design and analyze experiments (A/B tests, causal inference) to measure safety feature impact (e.g., login & verification, AI moderation support).
Quantify tradeoffs between operational efficiency, safety, and user growth/experience. Guide TnS team on key tradeoffs in decision-making
Own Cross-Functional Partnership
Influence decision-making through data storytelling and insights.
Standardize analytical methodologies and tools for scalable decision-making.

experience by developing strong data foundations, robust metrics and measurement frameworks, and advanced analytics and modeling that improve harm prevention and detection and moderation effectiveness.
Bachelor’s or Master’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
experience working with large-scale data and statistical analysis, including 1+ year of data science
experience in fraud prevention, moderation, or risk.
Proficiency in SQL and at least one scripting language (e.g., Python or R ).