Role Summary

The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will spend the majority of their time working on a data science project overseen by that mentor. The expectation is that at the end of the internship, functional and demonstrable progress has been made in a way that can be integrated into the functional portfolio of the team.



Important: the data science internship does not necessarily transition to a full-time role at the end of the period. Depending on team need, firm budget, and fit, interns should not expect their time to conclude with an offer.



Here are some example projects that may be prioritized for an internship:

Anomaly detection using deep neural networks

Numerical optimization applied to problems in manufacturing

Personal identifiable information (PII) and personal health information (PHI) detection in natural language

Areas of Focus

The data science internship is intended to expose the intern to what it means to do data science on a team in an enterprise setting. To that end, there are three major components with which an intern should expect to engage.

Modeling

Understanding how to frame business problems as data science problems

Navigating the full data science lifecycle: research and exploration, development, deployment, support

Using correct model selection and training procedures to prevent common modeling errors

Creating reasonable test scenarios and diagnostic measures

Software Development

Writing high quality Python (readable, reusable, modular, and well-abstracted code)

Using Linux, the terminal, and other core development tools

Understanding source control (git), containerization (docker) and deployment technologies (cicd/k8s)

Participating in code reviews

Business Value and Processes

Contributing to team development processes and agile/scrum rituals

Helping to clarify and prioritize work, define success criteria, and own work item completion

Applying all relevant change management controls for risk and compliance policies

Presenting work clearly to technical and non-technical audiences

Relevant majors and areas of expertise

Data Science

Computer Science

Mathematics

Natural or Social Sciences

Relevant additional backgrounds also considered