Senior Data Analyst (Risk Analytics)

• As a Senior Analyst on the Risk Analytics team, you will be the analytical engine behind our fraud prevention strategy
• You will build models, run experiments, and develop tools that help us make smarter, faster decisions, reducing reliance on external black boxes and static rules
• You will work closely with the Manager, Risk Analytics, owning the technical and statistical work that turns strategy into something measurable and executable. You will also be a key driver of how our team uses AI: not just adopting tools as they come, but actively building workflows, automating repetitive analysis, and thinking ahead about how AI can keep us one step ahead of increasingly sophisticated fraud
• 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

Benefits

• Benefits: We provide access to high-quality health insurance coverage, ample paid time off, and a matching 401k program.
• Parental leave: Congrats on welcoming a bundle of joy into your life! We offer 16 weeks of leave in a 12-month period.
• Subscriptions: Health and wellness are incredibly important so we cover employee subscriptions to services like One Medical, Ginger.io and Headspace.
• Team events: Regular virtual team hangs & happy hours are essential, especially while we’re all working remotely. We also put our typical day-to-day work aside for company-wide hackathons 3-4 times per year.
• Ticket perk: SeatGeek employees receive a monthly ticket-purchase stipend for live events!
• Flexible work environment: Flexible work environment, allowing you to work as many days a week in the office as you’d like or 100% remotely. A WFH stipend to support your home office setup.- Comfort operating in ambiguity; you are expected to define the problem as much as solve it
• 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
• Solid statistical grounding: you can design statistically valid experiments, perform significance testing, assess model calibration, and communicate findings clearly to a non-technical audience
• Strong SQL; you can own complex data pulls, understand warehouse structures, and build views and models that others rely on
• 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
• Familiarity with fraud vendors such as Forter, Riskified, or Sardine is a plus; experience with Looker or similar BI tools is a plus
• 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