Staff Data Scientist
Mercury•San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States•🌍 Remote
Full-timeLead
👁️ 0 views•📝 0 applications•Posted 9/10/2026•Expires 10/10/2026
Get alerts for roles like this
More Staff Data Scientist roles in San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States — straight to your inbox. No account needed.
Applying to this role? Tailor your résumé to this job description in one click, then download it clean — no watermark, no subscription.
Job Description
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:
Required Skills
Machine LearningData AnalysisData QualityModel ValidationModel DeploymentDocumentationTestingMonitoringCollaborationTechnical Leadership
Prepare to Win This Role
Everything you need to ace the interview and negotiate top-of-band compensation.