About the role:

• Develop software solutions to provide clinically relevant metrics and indications from unobtrusive and non-invasive sensors.
• Coordinate with cross-functional teams to align business, clinical, and model requirements.
• Collaborate with clinical teams and external vendors to facilitate data collection.
• Apply digital signal processing techniques to physiological waveforms (PPG, ECG, SCG, etc.) and build novel machine learning algorithms for hemodynamic assessment.
• Design, implement, and evaluate models using both classical feature extraction and deep learning techniques.
• Manage multiple experiments in parallel.
• Perform statistical and subpopulation analysis on sensor and clinical data collected from clinical trials.

QUALIFICATIONS:

• MS/PhD in Electrical Engineering, Statistics, Bioinformatics, Computational Biology, Computer Science, Mathematics, or equivalent practical experience.
• 5+ years of experience in data analysis or related fields such as Data Scientist, Computational Biologist, or Bioinformatics.
• Experience with wearable health sensors and related biometric algorithms (e.g., PPG, ECG, respiratory signals).
• Expertise in digital signal processing techniques and AI/deep learning concepts.
• Strong ability to communicate requirements, manage tasks, and relay results to management teams.
• Understanding of good machine learning practices for medical device development.

PREFERRED:

• Experience in a healthcare or medical device environment.
• Proficiency in programming languages such as Python, R, or MATLAB.
• Familiarity with cloud computing platforms and tools.
• Demonstrated ability to work independently and as part of a team.
• Excellent problem-solving and analytical skills.