Mercury is hiring a remote Senior Data Scientist Risk ML!
Mercury, a fintech company offering banking services through partner institutions, is hiring a Senior Data Scientist to join its Risk team. The role centres on machine learning for fraud and financial-crime defence: detecting, monitoring, and mitigating first- and third-party fraud, while also understanding customer behaviour and watching for financial crime risk. The posting frames the work as detail-obsessed and high-stakes, and the hire will sit at the intersection of product, engineering, and risk during a period of rapid company growth. It is a remote position open to candidates in the US or Canada, and the employer describes the compensation and equity package as competitive and benchmarked against current market survey data.
Key responsibilities
- Build, validate, and deploy machine learning models that identify and block fraud in real time
- Make models reproducible and robust through documentation, testing, and ongoing monitoring
- Maintain data quality and reliability across pipelines and tooling
- Partner with Risk Strategy to shape model inputs and applications, and with Engineering on deployment and observability
Key requirements
- 5+ years analysing large datasets to solve problems with measurable impact, including 3+ years of machine learning work
- Proficiency in SQL, including handling imperfect or messy data
- Proficiency in Python with statistical modelling and machine learning experience
- Hands-on experience deploying and monitoring ML models in production
- Comfort operating in a fast-moving environment with shifting priorities
- Nice to have: 1+ years of risk experience, familiarity with LLMs or GenAI for risk and fraud use cases, modern data tooling such as dbt, model governance in regulated industries, and zero-to-one greenfield delivery
- Seniority: Senior
- Work mode: Remote
- Salary: US employees (any location): $166,600 — $250,900 USD; Canadian employees (any location): $157,400 — $237,100 CAD
- Key skills: Python, SQL, machine learning, statistical modeling, model deployment, model monitoring, fraud detection, risk, dbt, LLMs, GenAI, model governance
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