Salary
💰 $100,000 - $150,000 per year
About the role
- Champion a data-first approach across internal teams and client engagements, promoting clarity and impact
- Build and deploy machine learning models to prevent fraud across diverse fintech use cases
- Use data and models to support the development of risk mitigation strategies and interventions while preserving and improving the user experience
- Work directly with clients to understand challenges and deliver high-impact, data-driven solutions
- Evolve our risk metrics, the supporting datasets, and how we measure the causal impact of initiatives
- Collaborate with engineering to scale models into production and optimize performance
Requirements
- 7+ years of experience in data science or quantitative modeling, ideally in risk or fraud contexts
- Advanced degree in a quantitative field (Mathematics, Statistics, Computer Science, Engineering, Economics, etc.)
- Strong working knowledge of Python, R, Spark, SQL, or equivalent
- Sharp critical thinking and creative problem-solving skills with a bias toward action
- Proven ability to explain complex technical findings to non-technical stakeholders and clients