Data Science Intern: AI-Driven Projects & Mentorship

Crowe; Boston (MA); On-site; USD 37,000 - 58,000; Full time

Job summary

Crowe is offering a Data Science internship aligned with the firm’s calendar, typically overlapping the summer break. Interns work under a mentor on real data science projects and aim to deliver demonstrable progress for potential integration into the team’s portfolio.

Opportunities include exposure to modeling, analytics, and software development in an enterprise setting, with emphasis on learning lifecycle stages, code quality, and collaborative problem solving.

Qualifications

• Pursuing or recently completed degree in Data Science, Computer Science, Mathematics, or related fields.
• Experience with Python programming and data processing.
• Familiarity with Linux, Git, and containerization (Docker/Kubernetes) is preferred.
• Ability to work on a mentor-guided data science project in an enterprise setting.
• Strong communication and collaboration skills.
• Willingness to learn and apply modeling, analytics, and software development practices.

Responsibilities

• Assist with data science projects under a designated mentor.
• Contribute to modeling, experimentation, and evaluation workflows.
• Develop and maintain readable Python code and data pipelines.
• Collaborate with teams on deployment and tooling improvements.
• Participate in code reviews and agile/scrum rituals.
• Present findings to technical and non-technical audiences.

Skills

Python

Linux

Git

Docker

Kubernetes

Data Science

Education

Data Science

Computer Science

Mathematics

Natural or Social Sciences

Tools

Git

Docker

Kubernetes

Job description

Crowe is offering a Data Science internship aligned with the firm’s calendar, typically overlapping the summer break. Interns work under a mentor on real data science projects and aim to deliver demonstrable progress for potential integration into the team’s portfolio.

Opportunities include exposure to modeling, analytics, and software development in an enterprise setting, with emphasis on learning lifecycle stages, code quality, and collaborative problem solving.