As a Data Scientist on the Card Payment Fraud Prevention team, you'll lead the charge against first-party fraud. You will build and deploy mission-critical machine learning models that operate across billions of transactions to secure the entire credit card portfolio.  You will research, build, and deploy advanced machine learning solutions using a cutting-edge tech stack.  Your work will directly translate to massive financial protection and business value from reduced credit losses. The mission includes optimizing models for highly challenging and expanding segments to improve fraud capture rates and enhance customer safety.

Team Description

The Card Payment Fraud Prevention data science team detects and mitigates first-party fraud by building and deploying machine learning models that keep customer accounts safe and compliant. Leveraging big data and a modern tech stack—including Python, Spark, Ray, H2O, PyTorch, and Kubernetes—the team delivers production-ready insights with a focus on both speed and sustainable impact, combining deep experience in traditional ML with an appetite for AI-based development.

In this role, you will:

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love

• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data

• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation, monitoring, and supporting continuous model deployment and maintenance in a production environment.

• Collaborate on the design and maintenance of production data science solutions, including writing clear technical documentation and ensuring models adhere to software development best practices.

• Manage model risk and maintain regulatory compliance across the model lifecycle, which includes maintaining model inventory records, executing model testing and change control protocols, and collaborating on independent model validation and compliance risk assessments.

• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The Ideal Candidate is:

•  An expert. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You rapidly come up to speed to pair technical skills with subject matter expert in your domain, conveying knowledge and shaping next steps for both you and the team you work in.

• Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools, cloud computing platforms, Objected-Oriented Programming (OOP) principles, and testing frameworks.

• Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, time series, and common ML modeling methodologies, particularly black box models like GBMs.

• A data guru. “Big data” doesn’t faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. 

Basic Qualifications:

• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:

  • A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics

  • A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)  or an MBA with a quantitative concentration plus 3 years of experience performing data analytics

  • A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Preferred Qualifications:

• Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)

• At least 1 year of experience working with AWS

• At least 3 years’ experience in Python, Scala, or R

• At least 3 years’ experience with machine learning

• At least 3 years’ experience with SQL

• Demonstrated experience with big data and distributed computing, using Spark or another comparable framework

• Demonstrated experience with model risk governance

• Demonstrated experience technically leading and developing a team

• Demonstrated experience with both traditional machine learning and emerging GenAI techniques, with primary focus on traditional ML model development, not GenAI-only experience

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.