Data scientist/machine learning

Vor 4 Tagen

Basel, Schweiz Randstad - High Priority Vollzeit

Jobdescription 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 Ph D 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, Num Py, Scikit-learn, Tensor Flow/Py Torch, and XGBoost/Light GBM. Time Series Data: Practical experience with the processing, analysis, and modeling of time series data from physical sensors or monitoring devices.

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