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 You should have: • 5+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 3+ 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 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 *Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC. Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role. Total Rewards The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers. Our target new hire base salary ranges for this role are the following: US employees (any location): $166,600—$250,900 USD Canadian employees (any location): $157,400—$237,100 CAD Originally posted on Himalayas