Signify

Product circularity engineer

Signify
NL Eindhoven, NB, NL
Onsite 2026-07-02
Estimated salary · Eindhoven
~ €56,100 - €90,000
iampro estimate — the employer published no figure

Job description

Eindhoven, Netherlands Category Research \& Development Posted Date 07/01/2026 Job Id 364535 **About Signify** Through bold discovery and cutting\-edge innovation, we lead an industry that is vital for the future of our planet: lighting. Through our leadership in connected lighting and the Internet of Things, we're breaking new ground in data analytics, AI, and smart solutions for homes, offices, cities, and beyond. At Signify, you can shape tomorrow by building on our incredible 125\+ year legacy while working toward even bolder sustainability goals. Our culture of continuous learning, creativity, and commitment to diversity and inclusion empowers you to grow your skills and career. Join us, and together, we’ll transform our industry, making a lasting difference for brighter lives and a better world. **More about the role** **More about the role** This is an exciting **internship opportunity** to help Signify accelerate product circularity for our consumer portfolio. As a Product Circularity Data \& Analysis Intern in Eindhoven (High Tech Campus), the Netherlands, your first focus will be to build a clear baseline of **where we are today**: what circularity\-related data we already have for existing products and what it tells us. Based on this insight, you will help determine **what we still need** and **what we can realistically do** to improve product circularity (material use, recyclability and repairability). *Some tips to help you improve the job advertisement*: * Create an overview of **where we are** by collecting and consolidating available circularity data for existing consumer products (materials, components, packaging, and supplier information) into a structured dataset. * Assess product circularity (material use, recycled content, recyclability, repairability and end\-of\-life routes) and identify **data gaps**, assumptions and the key drivers—so we can determine **what we need** and **what we can do

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