Must have

• Python

• Polish

• English

Requirements description

Requirements

• Proven experience in biomedical signal processing and algorithm development, preferably in a clinical or pharmaceutical context.

Experience applying data science/machine learning techniques for data analysis and model validation.

• Expert proficiency in a programming language commonly used for data analysis and signal processing (e.g., Python (with libraries like NumPy, SciPy, Pandas, scikit-learn) or MATLAB).
• Strong theoretical and practical understanding of digital signal processing (e.g., filtering, time-frequency analysis, feature extraction from time-series data).
• Familiarity with the principles and practical application of the V3+ framework or similar validation standards for digital health technologies/biomarkers.
• Excellent data management, statistical analysis, and data visualization skills.

Highly Desired

• Specific experience with IMU data for human activity, gait analysis or hand movement kinematics.
• Familiarity with clinical trial processes and regulatory requirements for digital health tools.
• Knowledge of Huntington's Disease and/or Parkinson's Disease and their clinical assessments (e.g., motor scores).

Offer description

We are seeking a highly motivated and skilled Biomedical Engineer to join our Data Insights Team. This role is critical for the development and validation of digital biomarkers used in our clinical trial products, particularly focusing on movement disorders like Huntington's Disease (HD) and Parkinson's Disease (PD). The ideal candidate will bridge the gap between biomedical signals and data science, transforming raw sensor data into meaningful clinical insights.

Your responsibilities

1. Provide expert knowledge and hands-on application of signal processing techniques for diverse biomedical data streams, including: Inertial Measurement Unit (IMU) data (accelerometry, gyroscope) for movement analysis, Touch screen interaction data (e.g., typing speed, swipe kinematics), Audio data (e.g., speech analysis).
2. Develop, refine, and implement novel signal processing (and machine learning) algorithms to extract clinically relevant features and create robust digital biomarkers.
3. Collaborate closely with digital biomarker science and engineering teams to define algorithm requirements and performance specifications.
4. Apply data science methodologies to validate digital biomarker algorithms using real-world patient data from clinical trials.
5. Implement and manage the V3+ framework (Verification, Validation, and Usability/Utility) or similar best practices for the rigorous evaluation and documentation of digital biomarker performance in disease populations.
6. Perform statistical analysis, modeling, and data visualization to communicate algorithm performance, robustness, and clinical relevance.

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