Data Analytics/Data Science Intern at Revvity
Boston, Massachusetts, USA -
Full Time


Start Date

Immediate

Expiry Date

25 Oct, 25

Salary

0.0

Posted On

26 Jul, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Statistics, Data Science, Power Bi, Data Wrangling, Data Analytics, Excel, Python

Industry

Information Technology/IT

Description

OVERVIEW:

We are seeking a highly motivated and detail-oriented Data Analytics/Data Science Intern to join our Operations team at Revvity. In this role, you will contribute to the development and automation of data-driven solutions that enhance manufacturing efficiency and process insight across our sites in Hopkinton, MA or in Boston, MA with a hybrid work schedule. This is a six-month full-time internship or COOP opportunity starting in late August.
This is a hands-on opportunity to apply data science and automation tools to real-world operational challenges. You’ll work alongside engineers and product managers to streamline data collection, implement monitoring systems, and support root cause analysis using prebuilt machine learning models and automation technologies.

BASIC QUALIFICATIONS:

  • Currently pursuing a Bachelor’s degree in Data Science, Data Analytics, Statistics, Engineering or a related field.

PREFERRED QUALIFICATIONS:

  • Prior internship experience preferred.
  • Foundational understanding of machine learning, data wrangling, and statistical analysis.
  • Familiarity with Power Automate, Power BI, Excel, Python or similar data tools/platforms.
  • Strong analytical and problem-solving skills with the ability to break down complex issues.
  • Excellent communication and collaboration skills.
Responsibilities
  • Automate data collection processes across the manufacturing site using Power Automate, APIs, and custom scripts where necessary.
  • Integrate and deploy machine learning models to monitor and analyze manufacturing process data.
  • Design and build monitoring dashboards and performance metrics tailored to various product types and manufacturing lines.
  • Develop key performance indicators to enable early detection of anomalies and support troubleshooting efforts.
  • Collaborate with cross-functional teams, including engineering, IT, and quality assurance, to ensure data accuracy and alignment with operational needs.
  • Document workflows, solutions, and best practices to support future scalability.
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