Machine Learning Product Manager - (f/m/x) - remote

Remote

Founded in 2017, refurbed is Europe’s fastest-growing marketplace for refurbished products, active in 23 European countries and having surpassed €2Bn in GMV — all while being profitable. With beautiful headquarters in Vienna, we operate as a remote-first company and have been recognised as a Top DACH Employerby Kununu for three consecutive years.

Our mission is to make sustainable consumption the easy choice by enabling customers to buy products up to 40% cheaper while significantly reducing CO₂ emissions.

If you thrive in an environment that values momentum, ownership, and impact, you’ll feel at home here. We’re a fast-paced, high-performance team that works hard and challenges itself everyday. To enable this high-performance every team member enjoys full autonomy over their location (we’re remote-first).

We're looking for a Machine Learning Product Manager to lead the strategy and execution of refurbed’s intelligent decision-making capabilities. Working closely with Data Science, Engineering, Growth, Commercial, and Marketplace teams, you'll turn complex business challenges into scalable, ML-powered product solutions.

This is a strategic and technical role where you'll define how intelligent systems make decisions, balance automation with human oversight, and drive measurable business impact through data-driven optimization and experimentation.

WHO YOU ARE:

5+ years of product management experience, with a few years in ML, AI or algorithmic decisioning.
Strong ability to work with Data Science and Engineering teams on ML-powered products, including translating business problems into model objectives, product requirements, and measurable outcomes.
Experience with marketplace dynamics, pricing systems, recommendation systems, ad tech, ranking is a strong plus.
Excellent analytical skills and a strong understanding of experimentation
Ability to reason through complex commercial trade-offs
Strong technical fluency: you do not need to be a data scientist, but you should be comfortable discussing data pipelines, model inputs and outputs, model evaluation, experimentation design, system constraints, and technical trade-offs.