Data Scientist for CGM Algorithm Development

Data Scientist for CGM Algorithm Development (m / f / d) - Data Scientist / Machine Learning Engineer / statistical principles / model validation techniques / Python / pandas / NumPy / Scikit-learn / Tensorflow / Pytorch / XGBoost / LightGBM / English Project For our pharmaceutical customer based in Basel, we are looking for a Data Scientist for CGM Algorithm Development (m / f / d) Background The Data Scientist will drive the feasibility evaluation, prototyping, design, and validation of novel algorithms for Continuous Glucose Monitoring (CGM) systems. This role is instrumental in translating complex physiological sensor data into accurate, clinically relevant insights, including integrating and interpreting diverse sensor and log data related to meal, insulin injections and physical exercise. This role requires a strong blend of statistical rigor, machine learning expertise, and creative problem‑solving to quickly evaluate and prove the technical viability of promising clinical concepts. The perfect candidate has minimum of 5+ years of hands‑on experience as a Data Scientist or Machine Learning Engineer and speaks fluent English. Additionally we are looking for someone who has experience or robust academic background (Master or PhD is highly desirable) in Data Science, Machine Learning or Statistics. 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 optimise machine learning models (e.g., XGBoost, Neural Networks, etc.). Write high‑quality, efficient, and reproducible Python code for data analysis, modelling, 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 modelling of time series data from physical sensors or monitoring devices. Nice to have Medical Domain Knowledge: Prior experience working with medical data, specifically in diabetes management (CGM / BGM), exercise physiology, or clinical nutrition data. Regulated Environment: Familiarity with the requirements and processes for software development in a regulated medical device environment. Big Data Tools: Experience with distributed computing frameworks like PySpark for handling very large datasets. #J-18808-Ljbffr