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

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

Strong SQL; you can own complex data pulls, understand warehouse structures, and build views and models that others rely on

Solid 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