Founded in 2017, refurbed is Europe’s fastest-growing marketplace for refurbished products, active in 12 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 our 250+ employees enjoy up to two months of workation per year. We’ve also been recognised as a Top DACH Employer by 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).
WHO YOU ARE:
- Bachelor’s degree in a quantitative field.
- Minimum of 5 years of experience in analytics.
- Proficiency in analytics and BI tools (eg. Looker, Tableau, or PowerBI).
- Experience working with large datasets and SQL for data extraction and manipulation.
- Strong experience with Python for data analysis, scripting, and statistical modeling.
- Familiarity with cloud-based data storage and tools (e.g. BigQuery).
- Intermediate understanding of statistical methods, including regression, clustering, and hypothesis testing.
- Intermediate understanding of statistics and how to interpret different outputs across various data science models.
- Proven track record of driving strategic impact through data-driven insights.
- Excellent problem-solving and critical-thinking skills with strong attention to detail.
- Effective communication and presentation skills to convey complex data insights to both technical and non-technical audiences.
WHAT YOU'LL DO:
- Analyze datasets to identify trends, uncover key performance drivers, and generate actionable insights.
- Collaborate with data engineering teams to improve and streamline data pipelines.
- Conduct statistical analysis to test hypotheses and validate findings.
- Apply statistical modeling techniques to solve business problems and improve decision-making processes.
- Translate complex data problems into clear solutions and communicate insights to stakeholders.
- Develop and enhance self-service capabilities to empower non-analytics stakeholders to access, understand, and explore data independently.
- Experiment with innovative approaches—such as AI-driven tools—to enhance speed and scale in analytics.
- Collaborate with cross-functional teams, including product, engineering, and marketing, to align analytics efforts with organizational goals.
WHY YOU WILL ENJOY WORKING WITH US:
Our Culture and