What makes you stand out You hold a degree in (business) mathematics, computer science, economics, or a comparable field.You also bring relevant professional experience in the data domain, for example as a BI Engineer or Data Engineer.You have strong knowledge of SQL and Python, as well as proven experience in developing cloud‑based data pipelines using Snowflake, Azure Data Factory, DBT, and Azure Data Lake.You stand out through your analytical skills and project management capabilities, as well as a strong goal‑oriented and problem‑solving mindset.You communicate confidently in German and English.
What makes you stand out You have a degree in Business Informatics, Computer Science, or a related field, along with at least three years of experience in business consulting, product management, or a similar business‑oriented environment.You bring proven experience in implementing AI or automation initiatives that deliver measurable business impact.You possess practical knowledge of Agentic AI, LLMs, prompt engineering, and other GenAI concepts.You have hands‑on experience with low‑code / no‑code tools such as Cognigy, Copilot Studio, Power Automate, and Power Apps.You have foundational data literacy (data sources, permissions, PII, telemetry) and can build simple impact dashboards in Power BI; experience with Azure AI Foundry or conversational AI platforms is a plus.You work collaboratively and independently, can translate complex technical AI topics into clear business language, and have strong English skills as well as German proficiency at least at B2 level.
elasticsearch AWS Python Google BigQuery Google Cloud Platform Numpy Pandas Gitlab What you will do Design and develop innovative algorithms to power a personalized shopping experience, leveraging cutting-edge machine learning techniques Deploy your solutions into production, taking full ownership and ensuring high performance and scalability Combine your data science expertise with a pragmatic, agile approach to find innovative solutions and drive measurable results within a fast-paced environment Challenge the status quo by identifying areas for improvement in existing retrieval and reranking systems, particularly those relying heavily on business logic, and propose data-driven solutions Thrive in a dynamic, fast-paced environment with a flat hierarchy, where your ideas and contributions can make a real difference Who you are Proficiency in Python or experience with at least one scientific computing language (e.g., MATLAB, R, Julia, C++) Strong SQL skills with experience in analytical or transactional database environments Theoretical understanding of machine learning principles, coupled with a hands-on approach to building and iterating on models Proven experience in building and deploying machine learning solutions that deliver tangible business value Strong understanding of data structures, algorithms, and tools for efficiently handling large datasets (e.g. pandas, numpy, dask, arrow, polars, …) Experience designing, building, and managing data pipelines Familiarity with cloud-based model training and serving platforms (e.g., GCP Vertex AI, Amazon SageMaker) Solid understanding of statistical methods for model evaluation Big Data: Experience analyzing large datasets using statistical and machine learning techniques DevOps: Familiarity with CI/CD tools (e.g., GitLab CI/CD, Hashicorp Terraform) is a plus Generative AI: Experience with generative AI and agentic frameworks (e.g., LangChain, ADK, CrewAI, Pydantic AI, …) is a plus Understanding of recommendation, retrieval and reranking systems in e-commerce and retail is a plus Excellent written and verbal communication skills in English Ability to effectively communicate complex machine learning concepts to both technical and non-technical stakeholders Proven ability to collaborate effectively within a team to establish standards and best practices for deploying machine learning models A proactive approach to knowledge sharing and fostering a quick development environment Nice to have Experience with BigQuery Knowledge of time series and (graph) neural network models Familiarity with statistical testing and Gaussian Processes Strong Knowledge of Computer Vision libraries, (e.g. OpenCV, TensorFlow, PyTorch) Experience maintaining Machine Learning pipelines through MLOps frameworks (e.g.