Job Description
We are on the lookout for a Staff Data Scientist to join our Quick Commerce Commercial Tribe. In this role you will be exposed to various topics related to our Quick Commerce business – assortment optimisation, content generation, promotion effectiveness improvement, and ranking. You will help other Data Scientists to deliver high quality solutions, challenge our assumptions and improve DS practices across our teams.
As a Staff Data Scientist, you will work closely with multiple cross-functional teams (Data Scientists, Machine Learning Engineers, and Software Engineers) to build and maintain highly scalable and reliable ML services deployed in dozens of countries globally with millions of customers.
If you're a skilled Data Scientist and creative problem solver who is eager to deliver solutions and hungry for a new adventure, an international workplace is waiting for you in the heart of Berlin!
1. Guide the discovery of new innovative data products from conception to operationalization to help optimize our systems and improve the experience for our users.
2. Keep yourself up to date and suggest state-of-the-art models to find out what works best for our customers.
3. Be the main reference point for new research and tech quality of Data Science in Qcommerce Commercial & Media Solutions tribes.
4. Enable collaboration and knowledge sharing by pushing cross teams’ projects.
5. Set up our teams for success by creating a long-term vision, a mid-term strategy, and several short-term roadmaps toward some of the most complex and interesting data problems.
6. Last but not least, you are not afraid to build global systems that are used by thousands of people and constantly strive for excellence.
Qualifications
7. Strong Statistical & ML Foundation – Comfortable with fundamentals, first-principles thinking, and ML theory across various domains.
8. Software Engineering Best Practices – Write maintainable Python code, ensuring high standards in production systems (observability, monitoring, performance).
9. Domain Expertise – Deep knowledge in at least one relevant area, such as causal inference, time series, Bayesian methods, NLP/LLM, ranking/matching, or advanced experimentation.
10. Data-Driven Approach – Carefully question assumptions before working on use cases and prioritize clean, reliable data.
11. Effective Communication & Agile Mindset – Filter and prioritize stakeholder requirements, focusing on MVPs and agile development.
12. Mentorship & Collaboration – Support team growth through mentoring while emphasizing reliability, disciplined experimentation, and data-focused development.
Nice to Have:
13. Leadership & Operational Excellence – Proven leadership in driving operational excellence and best practices within engineering teams.
14. System Design & Scalability – Experience designing and implementing large, scalable distributed systems using microservices and heterogeneous platform components, with knowledge of GCP.
15. Expertise in Testing & Knowledge Sharing – Strong background in A/B testing to analyze model effectiveness, with a record of knowledge sharing through conferences or publications.