Senior Data Scientist - Fraud

Mercury Co.Ltd • San Francisco, CA

16d • Remote

About The Position

Mercury is building the financial stack for startups. We're here to make banking intuitive, powerful, and safe for entrepreneurs and businesses of all sizes. We started by imagining what the best banking platform for startups would look like, and several years later we have hundreds of thousands of customers using Mercury's products. As we continue to scale, protecting our customers and Mercury from fraud - while still ensuring the seamless customer experience we have come to be known for - is critical. We are hiring a Data Scientist to join our Fraud and Limits team. This team is responsible for detecting, monitoring, and mitigating both first-party fraud (fraudulent applicants) and account takeover (ATO). 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

• 5+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 1+ years of relevant domain 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.
• The ability to balance high-leverage projects with foundational work such as reporting, dashboarding, and exploratory analyses.
• Comfort working in a fast-paced environment with evolving priorities.

Nice To Haves

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

Responsibilities

• Develop dashboards and monitoring systems to track key fraud and account health metrics.
• Conduct deep-dive analyses to understand fraud trends and behaviors, and translate findings into actionable recommendations.
• Build, validate, and deploy machine learning models to identify and prevent fraud in real time.
• Support ad hoc investigations and ensure data quality and reliability across pipelines and tools.
• Collaborate with Risk Strategy and Engineering to optimize rules, scoring systems, and other fraud defenses.
• Partner with cross-functional teams (Engineering, Product, Design, Operations) to embed data insights into decision-making.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed