INCLUSIVE BY DESIGN
At Zalando, our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce - one that is inclusive by design. We only assess candidates based on qualifications, merit, and business needs. We welcome applications from people of all gender identities, sexual orientations, personal expressions, racial identities, ethnicities, religious beliefs, and disability statuses. We only want to know why you’re great for this role, so please avoid including your picture, age, and marital status in your CV as well.
We want to provide you with a great candidate experience. Please feel free to inform us of any accommodations you may need, so we can best support and assist you throughout the hiring process.
do.BETTER - our diversity & inclusion strategy: https://jobs.zalando.com/en/our-culture/diversity-and-inclusion
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 28 markets across Europe and our +6,500 partner brands. To maintain this trust, it is vital for us to manage customer and purchase risks that originate from fraudulent 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 Risk and Abuse Management you will have the opportunity to join a dynamic and diverse group of engineers and scientists. As an analytics team, we are responsible for several predictive services 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.
WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)
Take end-to-end ownership for developing, deploying, and operating machine learning solutions for detecting, predicting, and managing customer and purchase risks
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
Conducting (ad-hoc) exploratory analysis based on big (un-/semi-)structured data to discover new suspicious behaviors on our fashion platform
Rigorous approach in solving, conducting, and documenting research projects
Work closely with software engineers, applied scientists, data analysts, product managers, and fraud specialists to solve the problem of fraud
Contribute to our growing science community and encourage knowledge sharing in an agile work environment
WE’D LOVE TO MEET YOU IF
1-3 years of 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
Ability and eagerness to understand the business context where the team operates and the customer problems being solved
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)
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)
Knowledge about machine learning on graphs, including community detection, graph embeddings, and graph neural networks.
OUR OFFER
Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.
Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners
2 paid volunteering days a year
Hybrid working model with up to 60% remote per week, actual practice is up to each team to best support their collaboration
Work from abroad for up to 30 working days a year
27 days of vacation a year to start for full-time employees
Relocation assistance available (subject to prior agreement)
Family services, including counseling and support
Health and wellbeing options (including Wellhub, formerly Gympass)
Mental health support and coaching available
Drive your development through our training platform and biannual peer-to-peer review
