Biomedical Data Scientist

at  Nirsense Inc

Anticipated Start Date: Immediately
Interested applicants please forward 1) Cover Letter, 2) Resume/CV, and 3) Three professional references to Careers@NIRSense.com.
About the Company:
NIRSense Inc is a medical technology company focused on creating world-leading wearable sensor systems to measure brain and muscle oxygenation. We’re a small, dedicated team seeking candidates interested in thoroughly understanding problems, excited about designing devices with broad applicability, and ready to transition those prototypes towards production and commercial use. We’re searching for a fellow enthusiast to play an integral role in expanding our engineering team and capabilities.
Job Summary:
We are seeking a Data Scientist to support data analysis within a large research portfolio involving military, clinical, and academic collaborations. This person will join a team developing and evaluating physiological monitoring wearable devices toward a definitive clinical trial in advance of 510(k) submission to the FDA. The ideal candidate possesses strong programming skills and is looking to apply these skills in the medical industry on human and animal physiological monitoring. They work well in a fast-paced and high-performance team composed of scientists and engineers.
Salary: Compensation package will be commensurate with the successful applicant’s experience. Anticipated hiring range: $90,000-120,000.
Hours: Full Time
Travel: Up to 5%

Job Requirements:

• Master’s degree or PhD in Biostatistics, Computer Science, Mathematics, or similar discipline with at least three (3) years of experience in working with biomedical data and signals for clinical protocols involving medical devices
• Solid coding experience, particularly in Python; C experience preferred but not required
• Experience working with large datasets and distributed computing on data extraction, cleaning, exploration, analysis and presentation using standard data analytics tools (e.g. pandas, NumPy, Matplotlib, scikit-learn etc.)
• Familiarity with Google Colab, Jupyter, MNE, Scipy, Tensorflow, R
• Familiarity with digital modeling and simulation tools
• Strong knowledge of statistical methods and machine learning fundamentals
• Experience with reading and interpreting relevant scientific literature
• Familiarity with code optimization techniques
• Excellent problem-solving and troubleshooting skills
• Experience or interest in working in a hybrid work environment
• Manage project assignments with a high degree of autonomy
• Strong written, verbal, and communication skills

Roles and Responsibilities:

• Prepare, clean, and analyze existing datasets for future scientific publications, reports and presentations
• Contribute to implementing signal processing, filtering, and data conditioning approaches relevant to project deliverables
• Draft specific scripts and algorithm implementations in Python
• Review existing code and recommend improvements/optimization
• Assist electronics engineers in translating offline scripts onto physical hardware
• Deliver or assist with progress reports as appropriate
• Maintain knowledge of the state-of-the-art in biosignal digital processing and noise mitigation methodologies, specifically in electrophysiology and biophotonics applications
• Work closely with and report to the Lead Scientist on project deliverables

• Prepare, clean, and analyze existing datasets for future scientific publications, reports and presentations
• Contribute to implementing signal processing, filtering, and data conditioning approaches relevant to project deliverables
• Draft specific scripts and algorithm implementations in Python
• Review existing code and recommend improvements/optimization
• Assist electronics engineers in translating offline scripts onto physical hardware
• Deliver or assist with progress reports as appropriate
• Maintain knowledge of the state-of-the-art in biosignal digital processing and noise mitigation methodologies, specifically in electrophysiology and biophotonics applications
• Work closely with and report to the Lead Scientist on project deliverable