Data Scientist (Machine Learning Engineer- CGM Algorithm Dev

• Design, develop, and validate predictive and analytical algorithms for CGM data.
• Create robust code using advanced machine learning and statistical techniques to assess technical feasibility.
• Model potential algorithmic approaches based on patient needs and real-world sensor data.
• Process and manage heterogeneous time series data from medical devices, including data cleaning, imputation, transformation, and feature engineering.
• Build and optimize machine learning models (e.g., XGBoost, Neural Networks) and write efficient, reproducible Python code for analysis and experimentation.
• Provide technical guidance within an Agile team framework and collaborate with multidisciplinary teams to achieve project goals.
• Present complex technical results and feasibility findings clearly to diverse stakeholders.

Proclinical is seeking a Data Scientist to contribute to the development and validation of innovative algorithms for Continuous Glucose Monitoring (CGM) systems. This role focuses on transforming complex physiological sensor data into meaningful clinical insights, integrating diverse datasets such as meal logs, insulin injections, and physical activity. The position requires a strong foundation in statistical analysis, machine learning, and creative problem-solving to evaluate and validate technical concepts effectively.

Please note that to be considered for this role you must have the right to work in this location or hold an EU passport., * Proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, XGBoost/LightGBM).

• Strong understanding of statistical principles, experimental design, and model validation techniques.
• Experience in processing, analyzing, and modeling time series data from physical sensors or monitoring devices.
• Background in Data Science, Machine Learning, Statistics, or a related quantitative field (Master's or PhD preferred).
• Ability to work effectively in a collaborative, multidisciplinary environment.