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.
Principal Accountabilities: Collaboration in projects of the European Data Science & Advanced Analytics Team.Concept, design, development and execution of complex innovative AI/Machine Learning solutions as well as execution and implementation of concept studies using advanced statistical methods.Development of deep learning models for structured medical concept extraction from unstructured data.Productionalization of machine learning algorithms in Big Data platforms.Application of modern data mining and machine learning techniques in connection with Healthcare Big Data to identify complex relationships and link heterogeneous data sources.Advanced usage of Large Language Models for summarization, chatbot, entity extraction etc.Develop foundational Deep Learning Models for assets and patients.Builds and trains new production grade algorithms that can learn from complex, high dimensional data to uncover patterns from which machine learning models and applications can be developed. Our Ideal Candidate Will Have: Master’s degree in Computer Science, Mathematics/Statistics, Economics/Econometrics or related field.Substantial years of professional experience in quantitative data analysis or PhD with at least 1 year of relevant professional experience with research in machine learning algorithms.Very good knowledge and in depth understanding of Machine Learning methods, both classical and deep learning models.Relevant experience with Natural Language Processing (NLP) models for extracting structured concepts from unstructured free text, including the design, training, and evaluation of information‑extraction pipelines.Very strong technical capability in Python, SQL, Hadoop ecosystem.Experience applying AI/Machine Learning methods to business questions.Very good knowledge of the higher statistical and econometric methods in theory and practice.Experience with handling Big Data.Ability to write clean, reusable, production-level codeExcellent communication skills (written and oral) including technical aspects of a project, ability to develop usable documentation, results interpretation and business recommendations.Strong analytic mindset and logical thinking capability, strong QC mindset.Knowledge of pharmaceutical market and experience with pharmaceutical data (medical, hospital, pharmacy, claims data) would be a plus, but not a must.Self-responsible for managing projects.Fluency in German & English.