Capital One is looking to solve the problem of unlocking big opportunities that help everyday people save money, time, and agony in their financial lives by leveraging the latest in computing and machine learning technologies and operating across billions of customer records.
Requirements
- Hands-on experience developing data science solutions using open-source tools and cloud computing platforms
- Experience with clustering, classification, sentiment analysis, time series, and deep learning
- Experience working with AWS
- Experience building production-grade agentic platforms, including RAG and graph-augmented systems, MCP or tool-calling integrations
- Demonstrated expertise in advanced model customization techniques—such as fine-tuning, parameter-efficient tuning (LoRA/QLoRA), reinforcement learning, or preference optimization
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
- Flex your interpersonal skills to translate the complexity of your work into tangible business goals
Other
- 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, A Master's Degree in a quantitative field, or a PhD in a quantitative field
- 5 years of experience performing data analytics for Bachelor's degree, 3 years of experience performing data analytics for Master's degree, or PhD in a quantitative field
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position
- Must be willing to work in one of the specified locations (McLean, VA, New York, NY, San Jose, CA, etc.)
