Product Manager – Fraud and Risk Management
| Company | Geico |
|---|---|
| Location | Seattle, WA, USA, Washington, DC, USA, San Francisco, CA, USA, San Jose, CA, USA, Fredericksburg, VA, USA, Bethesda, MD, USA |
| Salary | $100450 – $157850 |
| Type | Full-Time |
| Degrees | |
| Experience Level | Mid Level, Senior |
Requirements
- 3+ years of experience in product management, with a track record of delivering successful products in a fast-paced environment.
- Understanding of fraud and risk management including market trends, customer needs, and competitive landscape.
- Proven analytical and problem-solving abilities, with a data-driven approach to decision-making.
- Experience working with Agile methodologies and tools such as JIRA or Azure DevOps.
- Must be able to communicate effectively verbally and in writing.
Responsibilities
- Own and maintain the product backlog for fraud and risk management insurance solutions.
- Translate business and regulatory requirements into user stories with clear acceptance criteria.
- Collaborate with data scientists and fraud analysts to integrate predictive models and machine learning solutions into core products.
- Work with engineering to plan sprints, ensure delivery timelines, and optimize backlog health.
- Partner with internal stakeholders to identify fraud prevention and risk reduction opportunities.
- Conduct competitive analysis and stay informed on industry best practices and regulatory updates.
- Define product metrics, monitor performance, and iterate based on feedback and analytics.
- Serve as the subject matter expert on fraud and risk trends in the insurance domain.
Preferred Qualifications
- Strong understanding of fraud detection methodologies, risk scoring, and insurance claims workflows.
- Experience in P&C, financial, health insurance fraud systems.
- Knowledge of compliance standards (e.g., SOC 2, ISO 27001) related to risk data.
- Familiarity with third-party fraud/risk platforms (LexisNexis, FICO, FRISS).
- Experience working with machine learning models or collaborating closely with data science teams.
- Exceptional organizational skills with a proven ability to manage complex backlogs.