We are looking for a Senior Fraud Analyst with a passion for solving complex puzzles by investigating reactively and proactively data and live attacks. The solutions and insights you find will turn into proactive algorithms that make our community safer.
Here are a few of the things you'll do:
Discover and effectively communicate unique, actionable insights to prevent and detect attacks using our solution.
Extract data from internal systems, identify the correct metrics needed, create reports and analyze them to understand key trends.
Translate trends to prevention algorithm requirements.
Be the point of escalation to fraud investigators.
Proactively initiates and leads fraud‑risk projects from ideation through deployment, coordinating stakeholders across product, data, and engineering.
Here are a few of the things you'll do:
Discover and effectively communicate unique, actionable insights to prevent and detect attacks using our solution.
Extract data from internal systems, identify the correct metrics needed, create reports and analyze them to understand key trends.
Translate trends to prevention algorithm requirements.
Be the point of escalation to fraud investigators.
Proactively initiates and leads fraud‑risk projects from ideation through deployment, coordinating stakeholders across product, data, and engineering.
Requirements:
5+ years in payments / fintech fraud analytics or related risk science roles, with proven ownership of high‑impact programs in a data‑rich, large‑scale environment.
Expert SQL (window functions, CTEs, performance tuning) and strong programming in Python or R for data wrangling, statistical analysis, and model prototyping.
Deep knowledge of card‑not‑present (CNP) fraud typologies, ACH / RTP risk, chargeback management, and friendly fraud mitigation.
Demonstrated ability to translate complex quantitative findings into executive‑level insights and influence roadmaps across product, engineering, finance, and support.
Advanced data‑visualization skills (Looker, Tableau, Superset, or equivalent) with a portfolio of self‑service dashboards adopted by the company‑wide.
Exceptional communication skills in Englishable to brief C‑suite, advise legal/compliance, and write crisp documentation.
Leadership & mentorship track record: coached analysts, drove hiring, set career ladders, and fostered a culture of curiosity and accountability.
Regulatory fluency in PCI DSS, Nacha rules, Reg E/Z, and relevant US/state fraud‑reporting requirements.
Bachelors or Masters degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
Experience in building statistical models- Advantage.
Experience of developing and maintaining ML Models- Advantage.
Prior experience scaling fraud programs in a marketplace, SaaS, or creator‑economy context – Advantage.
Familiarity with graph databases (Neo4j, TigerGraph) and network‑analytics techniques for collusion detection- Advantage.
Working knowledge of Generative AI / LLM‑based tooling for analyst workflows and fraud pattern discovery- Advantage.
5+ years in payments / fintech fraud analytics or related risk science roles, with proven ownership of high‑impact programs in a data‑rich, large‑scale environment.
Expert SQL (window functions, CTEs, performance tuning) and strong programming in Python or R for data wrangling, statistical analysis, and model prototyping.
Deep knowledge of card‑not‑present (CNP) fraud typologies, ACH / RTP risk, chargeback management, and friendly fraud mitigation.
Demonstrated ability to translate complex quantitative findings into executive‑level insights and influence roadmaps across product, engineering, finance, and support.
Advanced data‑visualization skills (Looker, Tableau, Superset, or equivalent) with a portfolio of self‑service dashboards adopted by the company‑wide.
Exceptional communication skills in Englishable to brief C‑suite, advise legal/compliance, and write crisp documentation.
Leadership & mentorship track record: coached analysts, drove hiring, set career ladders, and fostered a culture of curiosity and accountability.
Regulatory fluency in PCI DSS, Nacha rules, Reg E/Z, and relevant US/state fraud‑reporting requirements.
Bachelors or Masters degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
Experience in building statistical models- Advantage.
Experience of developing and maintaining ML Models- Advantage.
Prior experience scaling fraud programs in a marketplace, SaaS, or creator‑economy context – Advantage.
Familiarity with graph databases (Neo4j, TigerGraph) and network‑analytics techniques for collusion detection- Advantage.
Working knowledge of Generative AI / LLM‑based tooling for analyst workflows and fraud pattern discovery- Advantage.
This position is open to all candidates.
