PhD Data Science Internship - Summer 2026

This is a rewarding full-time paid internship program lasting ten weeks from June through August 2026. To participate, applicants must be located in the continental United States and attend their designated location in person, as per Capital One's hybrid working model throughout the program. Successful interns will be evaluated for a potential full-time position with their team, and will collaborate with their manager to identify a start date upon graduation.

This internship is fully compensated. Please note that this opportunity does not include sponsorship for new applicants seeking employment authorization. However, upon completion of the internship, candidates may be considered for a full-time Data Science role that qualifies for employer immigration sponsorship based on business needs, market conditions, and other factors.

Basic Qualifications:

• Currently pursuing, or in the process of obtaining, a PhD in a quantitative field (such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Computer Science) with an expectation of completion by August 2028 or earlier.
• Must continue studies in the same PhD program following the internship.
• At least 6 months of experience or academic work using open-source programming languages for data analysis.
• At least 6 months of experience or academic work in machine learning techniques.
• At least 6 months of experience with either Python, R, or SQL.

Preferred Qualifications:

• Experience or academic background in inferential statistics.
• Familiarity with relational databases.
• Experience or academic work involving large-scale data analysis.

As a Data Scientist at Capital One, you'll join a high-performing team leveraging the latest computing technologies to create opportunities for everyday people to save money and improve their financial situations. You'll tackle complex financial service challenges by employing innovative techniques, and you'll make significant contributions from day one.

On any given day, you could:

• Assess open-source and internal modeling and analytics tools using actual business data.
• Integrate internal data with external data sources and APIs to extract and apply actionable insights.
• Design and build data science models to effectively communicate solutions to customers and company leaders.

We are looking for candidates who are:

• Intellectually curious: You inquire deeply, explore thoroughly, and are excited to innovate while mining data for insights into behaviors and sentiments.
• Builders: You are committed to enhancing customer experiences and products, taking ownership of your work.
• Experimental scientists: You enjoy exploring new methods, tools, and techniques, even if that means facing some failures along the way.
• Effective communicators: You excel at connecting with teammates and business partners to convey technical ideas in ways that resonate with various audiences.

Are you ready to be part of a community of talented individuals who tackle customer challenges with enthusiasm and creativity? If you're aiming for a long-term career at Capital One, this Data Science internship could be a fantastic start to your professional journey!

Salary information for this role, based on location, is as follows:

• Cambridge, MA: $174,000
• Chicago, IL: $158,000
• McLean, VA: $174,000
• New York, NY: $190,000
• Plano, TX: $158,000
• Richmond, VA: $158,000
• San Francisco, CA: $190,000
• San Jose, CA: $190,000

Note that salaries for roles in other locations will align with respective pay structures. Capital One offers a comprehensive and inclusive array of health, financial, and other benefits that support your overall well-being. To learn more about employment opportunities, please visit the Capital One Careers website. Eligibility for benefits may vary based on employment status and level.

This role is expected to accept applications for a minimum of 5 business days. Please, no agencies. Capital One is an equal opportunity employer, promoting diversity and inclusion in the workplace.