Data Scientist

vor 5 Stunden


Basel, Schweiz Randstad - High priority Vollzeit

Job Description 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 Design, develop, and validate predictive and analytical algorithms for CGM data. Understand patient needs and creatively model potential algorithmic approaches using real‑world sensor data. Apply expertise in processing and managing heterogeneous time‑series data originating from medical devices. Execute rigorous data cleaning, imputation, transformation, and sophisticated feature engineering. 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. 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. 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, XGBoost / LightGBM). Practical experience with the processing, analysis, and modeling of time‑series data from physical sensors or monitoring devices. We thank you for your application #J-18808-Ljbffr


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