We are looking for a Senior Fraud Analyst to serve as the technical cornerstone of our Fraud Prevention team.
In this role, you won't be managing a team; youll be managing the technical evolution of our fraud defenses. Reporting directly to the Fraud Prevention Director, you will act as a high-level individual contributor, bridging the gap between data science, engineering, and risk strategy. You will be responsible for pioneering the use of AI and advanced analytics to protect our community and ensure our platform remains the safest place for independent businesses to grow.
Here are a few of the things you'll do:
Technical Strategy & Architecture: Partner with the Fraud Prevention Director to define the long-term technical roadmap, moving us toward an AI-first, autonomous fraud detection ecosystem.
AI & LLM Integration: Design and implement Generative AI agents and LLM-powered workflows to automate complex forensic investigations, reducing the time-to-detect for emerging fraud vectors.
Advanced Detection Modeling: Develop and prototype sophisticated ML models and graph-based heuristics to identify collusion, synthetic identities, and fraud rings.
Cross-Functional Technical Lead: Serve as the primary technical consultant to Product and Engineering, ensuring that new payment features (like RTP or international expansion) are built with scalable, AI-driven safeguards from day one.
Expert Forensics: Act as an escalation point on high-stakes, large-scale fraud attacks, utilizing advanced Python and network analytics to deconstruct and mitigate threats.
Data Excellence: Set the standard for the fraud teams technical stack. You will optimize our SQL/Python environments and ensure our data infrastructure is capable of supporting real-time AI inference.
Requirements: Interested? Here's what we're looking for:
5+ years of experience in fintech or payments fraud analytics. You are a seasoned IC who has scaled fraud programs in complex, high-volume environments.
AI/ML Expertise: Proven experience leveraging Generative AI, LLMs (e.g., RAG, agentic workflows), and Machine Learning to solve real-world risk problems. You don't just use these tools; you know how to build with them.
Technical Powerhouse: Mastery of Python (for data science and automation) and SQL (performance tuning, CTEs, window functions). You are comfortable working alongside engineers and data scientists.
Network Analysis: Deep experience with graph databases (Neo4j, TigerGraph) and link-analysis techniques to identify organized criminal networks.
Demonstrated ability to translate complex quantitative findings into executive‑level insights and influence roadmaps across product, engineering, finance, and support.
Strategic Influence: While this is not a management role, you have a track record of influencing technical roadmaps and advising senior leadership on risk-reward trade-offs.
Advanced data‑visualization skills (Looker, Tableau, Omni, Superset, or equivalent) with a portfolio of self‑service dashboards adopted by the company‑wide.
Domain Depth: Expert knowledge of the payments lifecycle, including CNP, ACH, and RTP risk, as well as the specific fraud challenges of the SaaS and marketplace sectors.
Regulatory Fluency: A solid understanding of PCI DSS, Nacha rules, and the emerging regulatory landscape surrounding AI in financial services.
Prior experience scaling fraud programs in a marketplace, SaaS, or creator‑economy context - Advantage.
Education: Bachelors or Masters degree in a quantitative field (e.g., CS, Statistics, Mathematics, Economics) or equivalent technical experience- Advantage.
This position is open to all candidates.