Applied Scientist
vor 7 Monaten
**Applied Scientist **(all genders)**:
As** **Applied Scientist, you will be part of an expert team of software engineers and applied scientists working towards keeping the delivery time promise for all Zalando customers.
In your role, you will be converting real-world problems into mathematical models, and designing algorithms that solve these models. Beyond the algorithmic challenges, a significant part of your role will be the creation and maintenance of the software systems required to run these algorithms.
Here, you will have to ensure that the system adheres to the latest software engineering standards, and you will gain a comprehensive understanding of order processing systems to identify opportunities for improvement.
If you have a passion for creating scalable software systems and solving complex problems using discrete optimization, we want to hear from you. This is a unique opportunity to make a real impact and help drive the success of our business.
**INCLUSIVE BY DESIGN**
**WHAT WE'D LOVE YOU TO DO (AND LOVE DOING)**
- Contribute to modeling, solving, and implementing our research-driven software framework to cater for the increasing scale and load. Constantly question the model and the algorithms we use and seek for a constant improvement.
- Deliver end-to-end solutions and set the standard for the entire development cycle, including requirement engineering based on deep domain & business understanding, prototyping, implementing production software, as well as testing and operating the highly available production system.
- Collaborate closely with our software engineers to mutually influence and understand system constraints and opportunities, and take part in shaping the future of our system.
- Think abstractly and discover correlations between problems, methodologies, and solutions.
- Promote and support an inclusive culture and diverse team environment, fostering a positive and productive work environment for all team members.
**WE'D LOVE TO MEET YOU IF**
- You have a strong academic background of combinatorial optimization. You know the basic concepts of discrete optimization such as min-cost flows, linear
- and integer programming, the traveling salesperson problem, or the bin packing problems. And you are able to detect these concepts even if they are hidden in complex real-world problems.
- You prefer simple solutions over complex ones but at the same time, you are aware that complex problems sometimes require advanced algorithms.
- Knowledge about game-theory and/or machine learning is a plus.
- You have some experience in professional software engineering, ideally in a JVM-based language, and you are familiar with some of the standard concepts such as microservice architectures, design patterns, Spring Boot, REST, Kubernetes, architecture documentation, data and API specifications (e.g. OpenAPI), quality assurance and testing methods. You are eager to learn more about software engineering.
- You're excited about working in a setup that requires a deep domain understanding (e.g., how our logistics order processing works).
- You are a team player who thrives in an agile and cross-functional environment, and is passionate about fostering a positive and productive work environment.
- You are fluent in English.
**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 Zalando Lounge, discounts from external partners
- 2 paid volunteering days a year
- Hybrid working model with 60% (or more) 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
- Family services, including counseling and support
- Health and wellbeing options (including Gympass)
- Mental health support and coaching available
- Drive your development through our training platform and biannual peer-to-peer review
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