Data Scientist/Machine Learning

Are you a seasoned Data Scientist / Machine Learning Engineer driven by complex data and a passion for patient health? We are looking for you to take the lead in designing and validating predictive algorithms using cutting-edge techniques and time series data from medical devices, directly impacting diabetes care!

General Information:

• Start date: 1.2.26
• latest Start Date: 1.4.26
• Planned duration: 1.2.27
• Extension (in case of limitation): possible
• Workload: 100%
• Home Office: mostly onsite
• Working hours: Standard

Tasks & Responsibilities:

• Algorithm Design & Prototyping: Design, develop, and validate predictive and analytical algorithms for CGM data. Develop robust code using advanced ML and statistical techniques to prove technical feasibility.
• Feasibility & Ideation: Understand patient needs and creatively model potential algorithmic approaches using real-world sensor data.
• Data Pipeline & Feature Engineering: Apply expertise in processing and managing heterogeneous time series data originating from medical devices. Execute rigorous data cleaning, imputation, transformation, and sophisticated feature engineering.
• Technical Execution & Modeling: Build and optimize machine learning models (e.g., XGBoost, Neural Networks, etc.). Write high-quality, efficient, and reproducible Python code for data analysis, modeling, and experimentation.
• Collaboration: Provide technical guidance within an Agile team framework to junior data science colleagues. Work effectively within a multidisciplinary, distributed team to translate project goals into actionable data science tasks.
• Communication & Reporting: Synthesize complex technical results and present clear feasibility findings to diverse stakeholders.

Must Haves:

• Minimum of 5+ years of hands-on experience as a Data Scientist or Machine Learning Engineer. 
• Demonstrated experience or robust academic background (Master or PhD is highly desirable) in Data Science, Machine Learning, Statistics, or a related quantitative field. 
• Strong Statistical Foundation: Solid grasp of statistical principles, experimental design, and model validation techniques.
• Advanced Python Proficiency: Strong proficiency in Python and its core data science ecosystem: Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, and XGBoost/LightGBM. 
• Time Series Data: Practical experience with the processing, analysis, and modeling of time series data from physical sensors or monitoring devices.

We thank you for your application!