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Salary: USD 239,000 - 298,800 / annual
Mercury is a fintech platform providing banking, treasury, and financial workflow solutions for startups and technology companies. The company is hiring a Staff Data Scientist to join the Risk team, which is responsible for detecting, monitoring, and mitigating fraud while ensuring customer understanding and financial crime risk monitoring.
In this role, you will build, validate, and deploy machine learning models to identify and prevent fraud in real time. You'll support model reproducibility and robustness through documentation, testing, and monitoring. Key responsibilities include ensuring data quality and reliability across pipelines and tools, collaborating with Risk Strategy on model inputs and applications, and working with Engineering to optimize deployment and observability. You will act as a technical lead, prototyping and iterating on best practices while mentoring and upleveling the team.
You should have 7+ years of experience working with and analyzing large datasets, with at least 5+ years of ML experience. Proficiency in SQL and Python is required, along with experience in statistical modeling and machine learning. You must have demonstrated experience deploying and monitoring ML models in production, and the ability to lead and empower others. Strategic alignment across teams with differing roadmaps and timelines is essential.
Ideal candidates will have 1+ years of relevant risk experience, familiarity with LLMs or GenAI applications in risk/fraud detection, experience with modern data tools (e.g., dbt), knowledge of model governance in regulated industries, and experience building zero-to-one solutions in ambiguous problem spaces. This is an opportunity to work on critical challenges at a pivotal moment in Mercury's growth, collaborating across product, engineering, and risk to protect customers and the financial system.
About Mercury
Fintech — banking, treasury, and financial workflow platform for startups and technology companies.
