Innovation & Technology Internship - Data Science - Summer 2027 (Undergraduate)

CVS Health•New York, NY

3d•Hybrid

About The Position

The Innovation & Technology - Data Science Internship is a 10.5-week, full-time opportunity running from May 24, 2027, through August 4, 2027. It is a hybrid position based in New York, NY, with three in-office days per week, generally Tuesday through Thursday, and two days that may be worked remotely. Relocation assistance is provided for candidates who need to relocate more than 50 miles. As a CVS Health Corporate intern, you will work on projects that contribute to business success and take part in community service activities, professional development workshops, mentorship and networking opportunities, and interactions with senior leaders. The Innovation & Technology experience also includes a Leadership Insight Series, an Intern Innovation Challenge, and learning experiences covering presentations and storytelling, emerging technology, and AI tools. The Analytics & Behavior Change team applies advanced analytics, machine learning, behavioral science, and AI to health care and business challenges. As a Data Science intern, you will work alongside data scientists, engineers, and business partners to apply statistical methods and machine learning, analyze large datasets, build and evaluate predictive models, design experiments, and translate findings into actionable recommendations. The work supports areas including patient outcomes, health equity, digital experiences, and enterprise value. The internship pays $42 per hour based on a 40-hour workweek. It includes two days of paid leave and no work on the July 4 company holiday. Corporate interns may have an increased likelihood of receiving an offer for a future role with CVS Health. Work visa sponsorship is not available, including participation in CPT, OPT, or F-1 Visa programs.

Requirements

Currently pursuing a bachelor's degree with an expected graduation date between December 2027 and June 2028.
Knowledge of data science techniques, including statistical analysis, predictive modeling, and machine learning.
Experience using Python or R for data analysis and modeling.
Experience using SQL to query and analyze data.
Exposure to experimental design and quantitative methods, such as A/B testing and causal analysis.

Nice To Haves

Preferred fields of study include Statistics, Biostatistics, Mathematics, Data Science, Health Data Science, Operations Research, or a related quantitative field.
Familiarity with machine learning libraries and frameworks, such as scikit-learn, for building and evaluating models.
Experience analyzing large, complex datasets to identify patterns and insights.
Strong problem-solving and critical-thinking skills applied to ambiguous or open-ended analytical questions.
Experience communicating analytical findings through data visualizations, written summaries, and presentations to support decision-making.
Ability to clearly explain technical concepts and their implications to technical and non-technical audiences.

Responsibilities

Analyze structured and unstructured data to uncover patterns and insights.
Build, test, and evaluate predictive and machine learning models.
Design and analyze experiments to inform business decisions.
Create visualizations and communicate findings to technical and business audiences.
Contribute to solutions that improve health care quality, affordability, and outcomes.
Translate analytical findings into actionable recommendations.