Staff Data Scientist

Mercury•Portland, OR

1d•$239,000 - $298,800

About The Position

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 not only responsible 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.

Requirements

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

Nice To Haves

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

Responsibilities

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.