2d ago
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Quick Summary

Overview

About IPG Photonics IPG Photonics is the world leader in high-performance fiber lasers and photonic solutions used in manufacturing, medical, scientific, and communications applications.

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About IPG Photonics

IPG Photonics is the world leader in high-performance fiber lasers and photonic solutions used in manufacturing, medical, scientific, and communications applications. The Quality organization ensures that IPG products meet rigorous design, reliability, and performance standards. As a Quality Data Analyst Coop, you will play a key role in helping the team make data driven decisions to improve product quality and operational efficiency.

The Quality Data Analyst Co‑op will support the Quality department by collecting, analyzing, and interpreting data generated from manufacturing processes, testing operations, and customer feedback. This role is ideal for a student pursuing a technical or engineering degree with strong analytical skills and an interest in quality systems, data analytics, and continuous improvement.

Responsibilities

~1 min read
  • →Extract, clean, and analyze quality related datasets from manufacturing, test, and service systems.
  • →Create dashboards, charts, and visual reports to identify trends, anomalies, and root causes.
  • →Support investigation and troubleshooting efforts for nonconformities, yield issues, and process deviations.
  • →Maintain and update quality documentation, reports, and records.
  • →Support quality engineers with data preparation for corrective and preventive actions (CAPA), 8D reports, and internal audits.
  • →Collaborate with cross functional teams (Manufacturing, R\&D, Test, Supply Chain) to gather data and communicate findings.
  • →Recommend data driven improvements to enhance product quality and operational metrics.
  • →Develop clear, insightful data visualizations to support quality investigations, trend analysis, and reporting.
  • →Build and maintain dashboards using tools such as Excel, Power BI, or other industry standard visualization libraries.
  • →Transform large, complex datasets into user-friendly charts, graphs, and layouts that help engineers and leadership quickly understand issues and performance.
  • →Present visual findings during team meetings and help communicate quality metrics to cross functional stakeholders.
  • →Ensure consistency in formatting, labeling, and visual standards across recurring quality reports.

Requirements

~1 min read
  • Currently pursuing a degree in Engineering, Data Science, Statistics, Computer Science, Physics, or a related field.
  • Proficiency with Excel (pivot tables, functions, charts); experience with Python, R, SQL, or Power BI is a plus.
  • Strong analytical and problem‑solving abilities.
  • Ability to interpret data, draw conclusions, and present results clearly.
  • Detail‑oriented, organized, and able to handle multiple tasks.
  • Effective communication skills and ability to work in a team environment.

Nice to Have

~1 min read
  • Experience using statistical tools (SPC, Minitab, JMP, or similar).
  • Familiarity with quality methodologies (Lean, Six Sigma, Root Cause Analysis).
  • Exposure to manufacturing environments or complex electro‑optical systems.
  • Understanding of data visualization tools or dashboarding.

By the end of the co‑op, the student will gain experience with:

  • Real‑world quality and reliability engineering and data analytics.
  • Statistical methods used in high‑tech manufacturing.
  • Working cross‑functionally in a fast‑paced engineering environment.
  • Using data to support continuous improvement and reliability initiatives.

Location & Eligibility

Where is the job
Marlborough, United States
On-site at the office
Who can apply
US

About this job

Who can apply
United States
Posted
2 days ago
Employer
has 77 open jobs on Jobera in 2 countries.

Listing Details

Posted
October 30, 2026
First seen
November 2, 2026
Last seen
November 2, 2026

Posting Health

Days active
0
Repost count
0
Trust Level
57%
Scored at
November 2, 2026

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Intern, Professional