FAQs
Do we support remote work?
Yes, we do remote work in a hybrid format, allowing for 60% or more remote work per week depending on the team's collaboration needs.
What qualifications do I need to apply for this position?
You need a Master of Science or PhD focusing on Data Science, Statistics, Mathematics, or similar quantitative fields, along with a minimum of 2 years of industry experience.
What programming languages and tools should I be familiar with?
Strong experience with Python, including programming with numerical packages (numpy), data handling (pandas), and machine learning (scikit-learn) is required.
Is there any experience required in machine learning?
Yes, you should have sound theoretical and applied knowledge of machine learning and statistics and practical experience in deploying machine learning models into production.
What technologies should I know for big data handling?
Experience with Big Data technologies such as Apache Spark or Databricks, as well as relational databases like PostgreSQL, is expected.
What kind of projects will I be working on?
You will be working on classification, forecasting, and anomaly detection problems, developing scientific roadmaps to tackle complex challenges in the fashion industry.
Are there opportunities for professional development in this role?
Yes, you will collaborate with other Applied Scientists and engineers, contributing to a scientific roadmap that emphasizes continuous learning and innovation.
What are the benefits offered by Zalando?
Benefits include an employee shares program, discounts on products, 27 days of vacation, hybrid working model, mental health support, and family services among others.
How does Zalando support diversity and inclusion?
Zalando aims to be inclusive by design, not discriminating based on gender identity, sexual orientation, ethnicity, or disability status, and values diverse applications based on qualifications and merit.
Is relocation assistance available for this role?
Yes, relocation assistance is available subject to prior agreement.