Senior Applied Scientist - Transaction Risk Management (all genders)

Join Zalando as a Senior Applied Scientist in Transaction Risk Management

At Zalando, our vision is to be the Starting Point for Fashion. We aim to deliver a shopping experience built on trust for over 50 million customers in 25 European markets and our 6,500+ partner brands. To uphold this trust, it's crucial for us to manage customer and purchase risks arising from fraudulent activities on our fashion platform.

With 3.3 million shopping items, leading to hundreds of thousands of orders daily, we leverage big data and advanced machine learning techniques to predict and mitigate these risks, ensuring trustworthy relationships with our customers and partners.

About the Role

As a full-stack applied scientist in our Transactions Risk Management team, you will join a dynamic and diverse group of engineers and scientists. Our analytics team is responsible for several predictive services to safeguard other teams in Zalando’s checkout domain. Here, you will work on cutting-edge projects, raise technical standards, improve operational excellence, and shape our workflows.

Responsibilities

• Develop, deploy, and operate 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 in developing algorithms and executing them efficiently on resource-constrained platforms.
• Monitor and optimize machine learning infrastructure on AWS and Databricks/Spark.
• Conduct (ad-hoc) exploratory analysis based on big (un-/semi-)structured data to uncover new suspicious behaviors on our platform.
• Adopt a rigorous approach in solving, conducting, and documenting research projects.
• Collaborate closely with software engineers, applied scientists, data analysts, product managers, and fraud specialists to address fraud issues.
• Contribute to our growing science community and promote knowledge sharing within an agile work environment.

Requirements

• 3-5 years of hands-on experience as an applied scientist, developing and productionizing machine/deep learning models in cloud environments (preferably AWS).
• Proficiency in Python and related machine/deep learning frameworks such as Pytorch, Tensorflow, Keras, etc.
• Expertise in machine learning infrastructure and tools, including Databricks, Spark, Flink, AWS SageMaker, S3, EC2, Step Functions, and Git.
• Experience with data storage, ingestion, and transformation, including machine learning workflow orchestration.
• Passion for developing clean, maintainable, and testable code.
• Motivation for ongoing personal development in new technologies and software services.
• Ability to understand the business context where the team operates and the customer problems being addressed.
• Strong communication skills to convey analytical/engineering decisions and outcomes to a broader, non-technical audience.

Preferred Qualifications

• Experience with un-/weak-labeled data (self-supervised models, synthetic label generation).
• Experience in designing, developing, and operating highly-scalable microservices on distributed systems.
• Knowledge of automated deployment and monitoring through CI/CD pipelines (Docker, Kubernetes, etc.).
• Experience with high levels of test automation (unit, component, integration).
• Familiarity with running and evaluating experimental machine learning deployments (canary, blue-green).
• Understanding of machine learning on graphs, including community detection, graph embeddings, and graph neural networks.

Perks at Work

• Culture of trust, empowerment, and constructive feedback.
• Open-source commitment, meetups, game nights, 70+ internal technical and fun guilds.
• Knowledge sharing via tech talks, internal tech academy, blogs, product demos, parties, and events.
• Competitive salary, employee share shop, 40% Zalando shopping discount, discounts from external partners.
• Centrally located offices, public transport discounts, great IT equipment.
• Flexible working hours, additional holidays, and volunteering time off, free beverages and fruits, diverse sports, and health offerings.
• Comprehensive onboarding, mentoring, and personal development opportunities with an international