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).
Navigate the tradeoff between operational efficiency, safety, and user growth/experience.
Build dashboards and reporting frameworks to track platform health and safety performance.
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.
Design and analyze experiments (A/B tests, causal inference) to measure safety feature impact (e.g., login & verification, AI moderation support). Clearly communicate findings to technical and non‑technical stakeholders.
Quantify tradeoffs between operational efficiency, safety, and user growth/experience. Guide TnS team on key tradeoffs in decision-making
Partner with Product, Engineering, Operations, Policy, and Legal teams to define safety strategy.
Influence decision-making through data storytelling and insights.
Standardize analytical methodologies and tools for scalable decision-making.

Bachelor’s or Master’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
5+ years of Data Science experience working with large-scale data and statistical analysis, including 1+ year of data science experience in fraud prevention, moderation, or risk.
Strong analytical and problem‑solving skills, with a track record to lead projects from concept to impact.
Proficiency in SQL and at least one scripting language (e.g., Python or R).
Expertise in experimentation and causal inference (A/B testing, cohort analysis, pre/post analysis) to evaluate product or policy changes in production environments.
Hands-on experience with standard Machine Learning and statistical methods (e.g., prediction, classification, anomaly detection, time series), ideally in risk or fraud prevention contexts.
Ability to collaborate cross‑functionally with Product, Engineering, Operations, and Legal/Policy partners; comfortable influencing without direct authority.
Strong communication, with the ability to translate technical concepts to non‑technical stakeholders, including operations leaders and executives.

Senior Data Scientist – Fraud Prevention

Key skills