Senior Risk Data Analyst

Roblox Roblox · Consumer · San Mateo, CA · Engineering

Senior Risk Data Analyst role focused on payment fraud prevention and risk analysis within a high-volume transactional environment. Responsibilities include monitoring transactions, developing detection strategies, collaborating with cross-functional teams (engineering, product, data science), conducting deep-dive investigations, and analyzing data to identify fraud patterns. Requires proficiency in SQL and Python for data analysis and visualization, and experience with various payment risks.

What you'd actually do

  1. Monitor transaction activity and implement detection strategies to prevent fraud.
  2. Collaborate with cross-functional teams, including engineering, product, and data science, to develop and improve fraud prevention measures.
  3. Implement, monitor, and tune detection strategies and rules in production to prevent and mitigate payment fraud, minimizing false positives and maximizing loss prevention.
  4. Collaborate closely with cross-functional teams—including Engineering, Product, Data Science, and Finance—to define, scope, and implement new fraud prevention tools and systemic improvements.
  5. Conduct deep-dive investigations into emerging risk vectors, documenting findings and preparing regular, actionable reports on financial risk trends, loss rates, and mitigation effectiveness for leadership.

Skills

Required

  • 5+ years of professional experience in an analytical role, with a focus on fraud prevention and risk analysis in a high-volume transactional environment (e.g. e-commerce, fintech, or gaming).
  • Proficiency with data analysis tools and techniques, including advanced SQL and Python for data exploration, data visualization, and statistical analysis.
  • Direct experience analyzing and addressing different types of payment risk, such as card/fiat fraud, account takeover (ATO), chargeback analysis, and/or developer payout/earning fraud.
  • Strong analytical and problem-solving skills, with the ability to interpret complex data and identify trends.
  • Familiarity with risk prevention concepts, such as those in anti-fraud, anti-abuse, or trust and safety.
  • Excellent communication and stakeholder management skills, with the ability to collaborate effectively with cross-functional teams.
  • Degree in a quantitative field such as Data Science, Finance, Economics, Statistics, or a related field.

What the JD emphasized

  • fraud prevention
  • risk analysis
  • high-volume transactional environment
  • advanced SQL
  • Python for data exploration
  • statistical analysis
  • payment risk
  • anti-fraud
  • anti-abuse
  • trust and safety