Senior Data Analyst, Risk Analytics

What You'll Do

• Build and maintain Python-based analyses, models, and data pipelines that support fraud decisioning, vendor evaluation, and internal risk scoring
• Design and run statistical experiments from hypothesis through measurement and communication of results, including A/B tests on routing changes, holdout experiments, and vendor performance assessments
• Develop and iterate on internal fraud risk models using SeatGeek transaction and vendor data; own model calibration, validation, and ongoing performance monitoring
• Actively use AI tools including LLMs, code generation, and agentic workflows to move faster and build smarter; help define how AI gets embedded into the team's analytical processes, and identify opportunities to automate work currently done manually
• Contribute to vendor performance analysis: assess score calibration, measure lift across segments, and surface findings that inform routing decisions and contract discussions
• Build and maintain dashboards and reports in Looker and Hex; develop SQL models and data views to support the team's analytical needs
• Monitor fraud and operations metrics, investigate anomalies, and escalate findings with a clear point of view on recommended actions
• Collaborate with Risk Ops agents, the manager, and cross-functional partners in Engineering, Payments, and CX to translate analysis into action

What You Have

• 3+ years of experience in fraud analytics, risk, fintech, or a quantitatively demanding analytical role
• Strong Python skills; you build end-to-end analyses and pipelines independently, and are proficient with pandas, scikit-learn, statsmodels, or equivalent libraries
• Solid SQL; you can own complex data pulls, understand warehouse structures, and build views and models that others rely on
• Strong statistical grounding: you can design statistically valid experiments, perform significance testing, assess model calibration, and communicate findings clearly to a non-technical audience
• Hands-on experience building, training, and validating classification models independently; familiarity with model evaluation methods, handling class imbalance, and translating model outputs into business decisions
• Genuine enthusiasm for AI tools: you actively use LLMs and code generation in your day-to-day work, think about how to design AI-assisted workflows, and take initiative in identifying where AI can replace manual effort
• Comfort operating in ambiguity; you are expected to define the problem as much as solve it
• Familiarity with fraud vendors such as Forter, Riskified, or Sardine is a plus; experience with Looker or similar BI tools is a plus

Perks

• Equity stake
• Discretionary annual bonus
• Flexible work environment, allowing you to work as many days a week in the office as you’d like or 100% remotely
• AWFH stipend to support your home office setup
• Unlimited PTO
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