Senior Data Scientist

Senior Data Scientist - Fraud

Mercury

Location:
United States; Canada , San Francisco ▼
New York
Portland

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

166600.00 - 208300.00 USD / Year

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Job Description:

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.

Job Responsibility:

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

Requirements:

5+ years of experience working with and analyzing large datasets to solve problems and drive impact
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 have:

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)

What we offer:

base salary
equity (stock options)
benefits

Additional Information:

Job Posted:
December 10, 2025

Employment Type:

Fulltime

Work Type:

Remote work

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