Manager, Data Scientist - Card Payment Fraud Prevention

Capital One • McLean, VA

6d • $179,400 - $245,600 • Onsite

About The Position

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. 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. 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.

Requirements

• 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 6 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 4 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) plus 1 year of experience performing data analytics
• At least 1 year of experience leveraging open source programming languages for large scale data analysis
• At least 1 year of experience working with machine learning
• At least 1 year of experience utilizing relational databases

Nice To Haves

• PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
• At least 1 year of experience working with AWS
• At least 4 years’ experience in Python for large scale data analysis
• At least 4 years’ experience with machine learning specifically developing models that have gone into production
• At least 4 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

Responsibilities

• 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

Benefits

• comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
• performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager