AI & Data Science Internship (MLOps) - Remote

Overview

The Department of Informatics and Analytics at Dana-Farber Cancer Institute seeks a motivated and talented Artificial Intelligence and Data Engineering Intern for our expanding AI & data team. Successful candidates will have the opportunity to contribute on high-impact healthcare projects existing at the cutting edge of cancer research and clinical care.

In Short

• Internship focused on AI and Data Engineering.
• Part-time (<20h/week) from May until June, then full-time (40 hours/week) from June 2024 until mid-to-late August.
• Opportunity to work on impactful healthcare projects.
• Utilize Databricks for AI model development and deployment.
• Develop dashboards to track team and project usage/cost.
• Automate dashboard generation for teams/projects.
• Potential to develop a cost forecasting model.
• Remote work available for candidates in the New England area.
• Access to a virtual machine for CPU/GPU intensive tasks.
• Flexible timing for internship projects based on mentorship and skills.

Requirements

• Proficient knowledge of SQL and Python.
• Ability to query logging data and construct data tables.
• Experience with dashboard automation.
• Strong analytical skills for cost tracking and forecasting.
• Ability to work independently and manage time effectively.
• Access to high-speed internet and necessary equipment.
• Interest in healthcare and cancer research.
• Willingness to learn and adapt to new technologies.
• Strong communication skills for collaboration.
• Team-oriented mindset.

Benefits

• Gain hands-on experience in AI and data engineering.
• Work on projects that impact cancer research and clinical care.
• Mentorship from experienced professionals in the field.
• Flexible work schedule.
• Opportunity for full-time employment after internship.
• Access to advanced tools and technologies.
• Networking opportunities within the healthcare and tech industries.
• Remote work flexibility for New England residents.
• Potential for skill development in data analysis and engineering.
• Collaborative work environment.