PhD Data Science Internship (Summer 2027) – Hybrid, Paid

Capital One; Chicago (IL); Hybrid; USD 144,000 - 176,000; Full time

Job summary

Capital One invites PhD students to apply for a 10-week Data Science Internship for Summer 2027, located in the continental United States with in-person attendance at a Capital One site. The program runs June through August 2027 and may lead to a full-time role contingent on business needs and performance.

Applicants should be pursuing a PhD in a quantitative field with graduation planned by August 2029 or earlier, and have substantial experience with data analysis, machine learning, and Python,

Qualifications

• Currently pursuing a PhD with expected degree by August 2029 or earlier in a quantitative field.
• Must be continuing in the same course of study after completion of the internship.
• At least 6 months experience or academic work using open source programming languages for data analysis.
• At least 6 months of experience or academic work using machine learning techniques.
• At least 6 months of experience with Python, R or SQL.

Responsibilities

• Evaluate open-source and internal modelling and analytics tools using real business data.
• Integrate internal data with external data sources and APIs to discover actionable insights.
• Design and craft data science models to communicate solutions to customers and leadership.

Skills

Intellectually curious

Builder mindset

Experimental scientist

Communicator

Education

PhD in quantitative field

PhD in progress with Aug 2029 or earlier

Tools

Python

R

SQL

Job description

Capital One invites PhD students to apply for a 10-week Data Science Internship for Summer 2027, located in the continental United States with in-person attendance at a Capital One site. The program runs June through August 2027 and may lead to a full-time role contingent on business needs and performance.

Applicants should be pursuing a PhD in a quantitative field with graduation planned by August 2029 or earlier, and have substantial experience with data analysis, machine learning, and Python,