Mercury is building the financial stack for startups and needs to detect, monitor, and mitigate fraud (first-party fraud and account takeover) to protect customers and the company while maintaining a seamless customer experience.
Requirements
- Proficiency in SQL and experience using it to understand and manage imperfect data.
- Proficiency in Python and experience with statistical modeling and machine learning.
- Experience deploying and monitoring machine learning models in production.
- Experience building zero-to-one solutions in ambiguous or greenfield problem spaces.
- Familiarity with LLMs or other GenAI and how they can be applied to risk or fraud detection.
- Experience with modern data tools for pipelines and ETL (e.g., dbt).
Responsibilities
- Develop dashboards and monitoring systems to track key fraud and account health metrics.
- Conduct deep-dive analyses to understand fraud trends and behaviors, and translate findings into actionable recommendations.
- Build, validate, and deploy machine learning models to identify and prevent fraud in real time.
- Support ad hoc investigations and ensure data quality and reliability across pipelines and tools.
- Collaborate with Risk Strategy and Engineering to optimize rules, scoring systems, and other fraud defenses.
- Partner with cross-functional teams (Engineering, Product, Design, Operations) to embed data insights into decision-making.
Other
- 5+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 1+ years of relevant domain experience.
- The ability to balance high-leverage projects with foundational work such as reporting, dashboarding, and exploratory analyses.
- Comfort working in a fast-paced environment with evolving priorities.
- Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer.
- We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs.
