Backed by Accel.
About the role
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.
What they're looking for
- 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
More about this role
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...
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