Python · R · SQL · Tableau · Machine Learning
About the role
We’re hiring a data-driven Risk Analyst with strong analytical skills and an investigative mindset. You’ll help reduce fraud losses, improve risk processes, and protect a positive member experience. Alongside your core analytics work, you will also be investigating selected fraud and dispute cases-bringing together case evidence, transaction and account data, and risk signals to support clear, well-documented outcomes.
You’ll work closely with Product, Engineering, Data Analytics, Operational and member Experience partners. The role is a good fit for someone who can move between portfolio-level questions and detailed case review, conduct ground level investigation and data queries , communicate what the evidence supports, and turn findings into better risk decisions and processes.
In this role, you can expect to
- Analyze fraud and loss trends (25%) - Explore data proactively and in response to loss events to identify drivers, quantify opportunities, and recommend ways to reduce losses while minimizing member friction.
- Investigate fraud and dispute cases (30%) - Review assigned cases from intake through resolution or escalation. Bring together relevant member statements, transaction and account history, merchant evidence where applicable, and risk signals. Assess evidence quality and consistency; document what is supported, what remains uncertain, and the recommended next step in accordance with established policies and decision authority. Understand agent SOPs and get familiar with using agent tools with the goal to improve AHT and decision accuracy.