Data Science Engineer Internship

CCC Intelligent Solutions•Chicago, IL

2d•$20 - $43

About The Position

Our program is designed to #CCCJumpstart your career! At CCC, you will work and learn alongside innovative and inspiring leaders and gain valuable technical experience while working on real business solutions in a corporate setting. You'll join the Data Engineering team that builds the data and AI foundation for the company: the curated, governed, well-modeled data that powers analytics, machine learning, and generative AI across CCC. Interns work on real pipelines and real datasets serving the auto-insurance industry and internal teams.

Requirements

In order to be considered for this role you are required to be in pursuit of an Associate, Bachelor's or Master's throughout your internship.
We are looking for candidates who offer strong collaborative skills and work well with a team.
A strong interest in computer science and/or related fields is essential to the success of each intern.
If you have knowledge, through coursework or project work, of Python, Machine Learning, or Statistics/ Modeling, this opportunity is right for you.

Responsibilities

Build end-to-end pipelines that turn raw data into fully curated, enhanced datasets ready for analytics and model training.
Work with data across all three shapes: structured (relational databases, transactional tables), semi-structured(JSON, XML, compressed/zipped files), and unstructured (claim photos, estimate PDFs, repair notes, audio, and video).
Produce reusable data building blocks, data models, and data flows for varying client demands — dimensional models, data feeds, dashboard reporting, and data science research and exploration.
Develop and optimize Spark jobs on Databricks, including Delta Lake tables and incremental and batch processing patterns.
Apply data governance practices using Unity Catalog and AWS Glue Data Catalog: cataloging and lineage, access controls, PII handling, schema management, and data quality validation.
Use Apache Airflow to build DAGs that handle cross-DAG dependencies, orchestrate concurrent pipelines, monitor tasks, and alert when SLAs are not met.
Partner with data scientists and ML engineers to make datasets model-ready, including preparing multimodal data for AI and computer vision use cases.