Machine Learning Intern

Role Summary

The Data Science (DS) internship at Crowe aligns with the firmwide calendar, typically overlapping the academic summer. Interns will be assigned a data scientist mentor and dedicate most of their time to a data science project overseen by that mentor. The goal is to achieve functional and demonstrable progress that can be integrated into the team's portfolio. Note: This internship does not guarantee a full-time role upon completion; transition to a full-time position depends on team needs, firm budget, and overall fit.

Example Projects

• Anomaly detection using deep neural networks
• Numerical optimization applied to manufacturing problems
• Personal identifiable information (PII) and personal health information (PHI) detection in natural language

Areas of Focus

The internship aims to expose participants to enterprise-level data science practices. Key components include:

Modeling

• Framing business problems as data science problems
• Navigating the full data science lifecycle: research, exploration, development, deployment, and support
• Applying correct model selection and training procedures to prevent common modeling errors
• Creating reasonable test scenarios and diagnostic measures

Software Development

• Writing high-quality Python code (readable, reusable, modular, well-abstracted)
• Utilizing Linux, the terminal, and other core development tools
• Understanding source control (git), containerization (docker), and deployment technologies (CI/CD, Kubernetes)
• Participating in code reviews

Business Value and Processes

• Contributing to team development processes and agile/scrum rituals
• Clarifying and prioritizing work, defining success criteria, and owning work item completion
• Applying 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
• Other relevant backgrounds will also be considered.

Key Responsibilities

• Conduct anomaly detection using deep neural networks.
• Apply numerical optimization to manufacturing-related challenges.
• Develop methods for PII and PHI detection in natural language.
• Frame business challenges as data science problems.
• Manage the end-to-end data science lifecycle (research, development, deployment, support).
• Implement correct model selection and training practices.
• Design and execute test scenarios and diagnostic measures.
• Write high-quality, maintainable Python code.
• Utilize Linux, terminal, and development tools.
• Work with git, docker, and CI/CD/Kubernetes.
• Engage in code reviews.
• Contribute to agile/scrum processes.
• Assist in clarifying and prioritizing tasks, defining success metrics, and completing assigned work.
• Adhere to change management controls, risk, and compliance policies.
• Present findings and work to diverse audiences.

Qualifications

• Uphold Crowe’s values of Care, Trust, Courage, and Stewardship.
• Act ethically and with integrity.