Machine Learning Engineering Graduate Internship - Summer 2025
Position Summary
This program is a 11-week full-time opportunity that provides real work experience with a team in your business area. You will be aligned to projects to lead or contribute to while also participating in networking, development, and career exploration activities. Our Corporate Interns can apply for future full-time roles during the program and have an increased likelihood to receive an offer for a post-graduation role.
The summer internship program is from May 28th – August 8th 2025.
CVS Health follows a 3 day in office (generally Tuesday, Wednesday, and Thursday) hybrid work model providing office-based colleagues the ability to flex between working in the office and working from home based on the work you need to accomplish. The other 2 days each week will be working remotely from home, office, or another location of your choice.
Our office locations are currently expected to be: New York City, NY, Wellesley (Boston), MA, Hartford, CT and Irving (Dallas), TX. A housing stipend may be provided, based on eligibility, and interns will be given the opportunity to connect with each other prior to the start of the program to coordinate housing if desired.
The hourly rate is $55 per hour with 40 hours/ week, no work on July 4th (company holiday), and two days of paid leave. Please disregard the range listed below.
Who we are
We are Americas leading health solutions company delivering care in ways no one else can. Our purpose is simple and clear: Bringing our heart to every moment of your health. At CVS Health, we possess an extensive repository of data that spans over 150 million individuals, providing an unparalleled foundation for ambitious Data Scientists and Engineers. In this role, your work will underpin data driven business decisions and contribute to our mission of delivering the industry’s best data products with a customer first mindset and team-oriented approach.
About the Business Area
Our organization, Analytics & Behavior Change, is an innovation engine supporting the entire CVS Health organization (Aetna health care business, retail, and pharmacy services) by embedding deep insights into key decision processes and focusing on our biggest, most complex problems. We partner with business leaders throughout the organization using advanced analytics tools, modeling, and machine learning to generate insights by using data to create meaningful impact. We're focused on creating new opportunities that drive change across the enterprise in areas such as:
Customer experience and behavior change
Digital health
Provider efficiency and effectiveness
Patient safety
Health care cost savings
Clinical services/Care management
Fraud, waste, and abuse detection/prevention
Analytics & Behavior Change has the feel of a startup within one of the nation's largest companies, and we're looking for people who are motivated by solving tough challenges to join us in transforming health care….one line of code at a time.
What you will do
Identify opportunities related to the data science lifecycle and develop solutions
Lead the development of a Python package for data scientists, which will also be open-sourced
Embrace a product and systems mindset when developing solutions at scale
Work cross-functionally with data scientists, data engineers, and leadership
Use data to inform strategic and design decision making in addition to measuring performance and outcomes to demonstrate efficacy
Required Qualifications
Foundational knowledge:
Machine learning techniques such as supervised, unsupervised, and deep learning.
Statistical tests and probability theory
Programming and software engineering:
Proficiency in Python
Experience in developing efficient, maintainable, and scalable code in a collaborative environment with version control using Git.
ML frameworks and libraries:
Experience in ML modeling with scikit-learn, PyTorch, Keras, TensorFlow, JAX, HuggingFace, etc.
Data and feature engineering:
Experience in preprocessing large datasets to generate high quality features for training and inference datasets.
Preferred Qualifications
Cloud computing and big data:
Familiarity with engineering features and deploying models on cloud platforms such as GCP, AWS, or Azure.
Domain Expertise:
Demonstrated expertise in one or more machine learning applications such as natural language processing, time series modeling, anomaly detection, computer vision, generative AI, etc.
ML Ops:
Experience with creating and deploying containerized ML pipelines with Continuous Integration/Continuous Deployment
Experience with creating ML pipelines with MLOps best practices such as drift monitoring, performance monitoring, continual learning, A/B testing, etc.
Education
Currently pursuing a Master’s degree program with an expected graduation date between December 2025 and August 2026
Preferred majors include Computer Science, Data Science, Mathematics, Statistics, Physics, Engineering, or related STEM discipline.