THE ROLE AND THE TEAM

At Zalando, our vision is to be the Starting Point for Fashion. We want to offer a shopping experience that is characterized by trust, for our more than 50 million customers in 25 markets across Europe, and also for our +6,500 partner brands. To maintain this trust, it is vital for us to manage transactional risks that originate from suspicious behaviors on our fashion platform. With 3.3 million shopping items, resulting in hundreds of thousands of orders every single day, we use big data and advanced methods from machine learning to predict and mitigate such risks and ensure trustful relationships with our customers and partners.
As a senior engineer in data and machine learning in our Transaction Risk Management team, you will have the opportunity to join a dynamic and diverse group of product managers, engineers, and applied scientists. As a machine learning team, we are responsible for several predictive services running in Java, Python, AWS, and Kubernetes to safeguard teams across Zalando. As part of our team, you will have the chance to work on cutting edge projects and innovative technologies, raise the technical bar, improve our operational excellence, and shape our ways of working.
What you build and put in production is impacting not only every single Zalando customer on the spot, but also the business performance of Zalando and its partners.

WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)

We are looking for a passionate and proactive senior engineer that has proficiency in both the data and machine learning domain. You will participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance. Contribute to our growing engineering and science community and encourage knowledge sharing in an agile work environment.

Data Engineering:

• Implement, operate, and continuously improve large-scale data pipelines for batch and real-time processing to enable risk inference and risk assessment
• Increase the operational excellence of our data pipelines by using best practices for data quality assurance, testing, monitoring, alerting, and cost efficiency
• Support the development of a feature store, consisting of features for risk inference and risk assessment
• Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards to drive key business decisions

ML Engineering:

• Deploy, operate, and continuously improve our large-scale machine learning services for detecting transactional risks, including batch and real-time inference, on cloud-based infrastructure
• Responsible for team’s end-to-end MLOps, productionizing machine learning services, including workflow automation and orchestration
• Tackle challenges for developing algorithms and running them efficiently on cloud-based infrastructure
• Increase operational excellence of our machine learning services by introducing best practices for model testing, experimentation, versioning, serving, retraining, monitoring and alerting