Senior Data Scientist - Fraud

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.

Here are some things you’ll do on the job:

• 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.

You should have:

• 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.

Ideally you also 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).

The total rewards package at Mercury includes base salary equity (stock options) 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): $166600 - 208300 USD

• Canadian employees (any location): CAD 157400 - 196800

*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.

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