🚀 Der perfekte Job: Principal Applied Scientist - Transactions Risk Management (all genders) bei Zalando in Berlin

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 abusive 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 full-stack applied scientist in our Transactions Risk Management department you will have the opportunity to join a dynamic and diverse group of 15-20 scientists and engineers. As an analytics team, we are responsible for several predictive services running in Java, Python, AWS, and Kubernetes to safeguard other teams in the checkout domain at Zalando. As part of our team, you will have the chance to work on cutting edge projects, 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 performance of Zalando and its partners.

WHERE YOUR EXPERTISE IS NEEDED

• Lead the applied science work across two teams in the Transactions Risk Management department, involving the research strategy, roadmap, and projects

• Take end-to-end ownership for developing, deploying, and operating machine learning solutions on our centralized abuse protection platform for detecting and managing transactional risks

• Raise the analytical and technical bar within the applied science domain by improving processes and documentation

• Quick prototyping and spiking of machine learning models to assess their applicability for solving research, customer, and business problems

• Tackle challenges for developing algorithms and running them efficiently on resource constrained platforms

• Rigorous approach in solving, conducting, and documenting research projects

• Work in a cross functional team consisting of software applied scientists, engineers, product managers, and product analysts

• Be an influential and empathetic leader and mentor for our applied scientist to help growing their expertise and curiosity in science and risk management topics

• Contribute to our growing science community and encourage knowledge sharing in an agile work environment

WHAT WE’RE LOOKING FOR

• Solid hands-on experience as an applied scientist, developing and productionizing machine / deep learning models in cloud environments (preferably AWS)

• Good proficiency in Python and related machine / deep learning frameworks, such as Pytorch, Tensorflow, Keras, etc.

• Expertise in machine learning infrastructure and tooling, such as Databricks, Spark, Flink, relational databases, AWS SageMaker, S3, EC2, Step Functions, Git

• Experience with data storage, ingestion, and transformation, also including machine learning workflow orchestration

• Passion for developing clean, well maintainable, and testable code

• Motivation for continued personal development in discovering new technologies and software services

• Curiosity and eagerness to understand the business context where the team operates and the customer problems being solved

• Very good communication skills to translate (even complex) analytical / engineering decisions and outcomes to broader, non-technical audience

Preferred

• Previous knowledge in working with un-/weak-labeled data (self-supervised models, synthetic label generation, anomaly detection)

• Experience in designing, developing, and operating highly-scalable microservices on a distributed system

• Knowledge in automated deployment and monitoring through CI/CD pipeline (Docker, Kubernetes, or similar)

• Work experience with a high level of test automation (unit, component, integration)

• Running and evaluating experimental machine learning deployments (canary, blue-green)

PERKS AT WORK

• Culture of trust, empowerment and constructive feedback, open source commitment, meetups, game nights, 70+ internal technical and fun guilds, knowledge sharing through tech talks, internal tech academy and blogs, product demos, parties & events