Data Scientist for CGM Algorithm Development
We are seeking a highly skilled Data Scientist to join our team and drive the evaluation, development, and validation of novel algorithms for Continuous Glucose Monitoring (CGM) systems.
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. As a Data Scientist, you will have hands-on experience with data analysis, machine learning, and algorithm development.
You should possess a solid grasp of statistical principles, experimental design, and model validation techniques. Strong proficiency in Python and its core data science ecosystem is also essential. Additionally, practical experience with time series data processing, analysis, and modeling from physical sensors or monitoring devices is required.
The ideal candidate will have advanced skills in:
* Statistical Foundation: A deep understanding 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.
Key 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.
- Build and optimize machine learning models.
- Collaborate with junior data science colleagues and work effectively within a multidisciplinary team to translate project goals into actionable data science tasks.
- Synthesize complex technical results and present clear feasibility findings to diverse stakeholders.
Requirements:
- Minimum of 5+ years of hands-on experience as a Data Scientist or Machine Learning Engineer.
- Demonstrated experience or robust academic background in Data Science, Machine Learning, Statistics, or a related quantitative field.
- Advanced Python Proficiency.
- Time Series Data Experience.