Staff Data Scientist - Risk ML

In 1999 NASA lost contact with its Mars Climate Orbiter after a 9 month journey from Earth. It began its planned orbital insertion maneuver but went out of radio contact after passing behind Mars. While we may never know whether it was destroyed in the atmosphere or re-entered heliocentric space, we can draw the lesson that getting the details (in this case, units) right is critical, especially when shooting for the stars.

While Mercury’s cosmic journey may be more metaphorical, we have our own sky-high ambitions and the need to marry those with precise data analysis.

To that end, we are hiring a Machine Learning-focused Data Scientist to support our Risk team. This team is responsible not only for detecting, monitoring, and mitigating both first- and third-party fraud but also ensuring we know and understand our customers while monitoring their behavior for financial crime risk. You’ll play a key role in strengthening our fraud defenses while ensuring that Mercury continues to deliver a smooth and trustworthy banking* experience.

This is an opportunity to join Mercury at a pivotal moment in our growth. You’ll be working on some of the most critical challenges facing the business and collaborating across product, engineering, and risk to protect our customers and the financial system at large.

Here are some things you’ll do on the job:

Build, validate, and deploy machine learning models to identify and prevent fraud in real time

Support the reproducibility and robustness of said models through documentation, testing, and monitoring

Ensure data quality and reliability across pipelines and tools

Collaborate with Risk Strategy to ideate on model inputs and applications and with Engineering optimize deployment and observability

Act as a technical lead prototyping, iterating on, and codifying best practices - and bringing the rest of the team along

You should have:

7+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 5+ years of ML experience

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

Comfort working in a fast-paced environment with evolving priorities

Demonstrated ability to lead and empower others, delivering not just on your own work, but upleveling those around you

The ability to drive strategic alignment between teams with differing roadmaps, timelines, or architectures

Ideally you also have:

1+ years of relevant risk experience

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)

Experience with model governance as required in finance or other regulated industries

Experience building zero-to-one solutions in ambiguous or greenfield problem spaces