Temporary Intern - Data Scientist

Cotiviti•Remote,

1d•$20 - $26•Remote

About The Position

We are looking for a curious, driven Data Science intern to join our analytics team for the summer. You will work on real healthcare data problems alongside full-time data scientists and engineers — not internal demos — touching everything from data wrangling and feature engineering to model evaluation and lightweight deployment. This is not a passive internship. Healthcare is a domain where accuracy, compliance, and explainability matter, and you will help shape how predictive and analytical models inform decisions for payers and providers. Expect to ship something you are proud of by the end of the summer.

Requirements

currently pursuing a Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related quantitative field
hands-on experience with Python for data analysis — pandas, scikit-learn, matplotlib
solid understanding of classification, regression, train/test discipline, and model evaluation
comfort writing queries against relational databases
experience taking a project end-to-end — question, data, model, insight — through coursework, personal projects, or prior internships
ability to explain technical work clearly to non-technical stakeholders
Communicating with others to exchange information.
Problem-solving and thinking critically.
Completing tasks independently.
Interpreting data.
Making timely decisions in the context of a workflow.
Maintaining focus.
Assessing the accuracy, neatness and thoroughness of the work assigned.
Learning new tasks and completing tasks in situations that have a speed or productivity quota.
Remembering and adhering to processes and protocols.

Nice To Haves

Exposure to model deployment using REST APIs, FastAPI, or similar
Familiarity with Git and basic experiment tracking
Experience with R, XGBoost, or other gradient-boosting libraries
Interest in healthcare data, payer/provider workflows, or regulated environments
Exposure to LLMs, fine-tuning techniques (e.g., LoRA), or evaluation methods

Responsibilities

Build and evaluate classification or regression models using Python and standard ML libraries
Engineer features from claims, clinical, or operational datasets
Measure model performance using AUC, log-loss, calibration, and other appropriate metrics
Clean, transform, and profile datasets; document data-quality findings and assumptions
Write SQL against relational databases to extract and shape analytical inputs
Communicate findings through clear visualizations and written summaries
Build lightweight APIs or endpoints to surface model outputs (e.g., FastAPI)
Contribute to version-controlled, reproducible workflows using Git
Document pipelines so teammates can extend and audit your work
Assist with experiments at the intersection of ML and healthcare workflows
Explore approaches such as adaptive fine-tuning or evaluation harnesses for LLM-based components
Complete all responsibilities as outlined in the annual performance review and/or goal setting.
Complete all special projects and other duties as assigned.
Must be able to perform duties with or without reasonable accommodation.
This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.