Spry Health, Inc.
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|Company:||Spry Health, Inc.|
|Location:||Palo Alto, California, US|
|Introduction:||We put a hospital room into a wristband so that extremely sick people don't have to go back to the hospital.
Spry Health closes the gap between complex, chronically-ill patients and care providers with continuous, remote vital sign monitoring. The Loop wearable uploads patient data to the cloud to be analyzed using evidence-based rulesets. Clinicians then receive timely insights about their most vulnerable patients to drive effective interventions.
|Field:||machine learning, signal analysis, statistics|
|Description:||Conduct research and development activities to allow for continuous measurement of vital sign using wearable technology, through use of signal analysis, optics, machine learning, and statistics
We are looking for an Algorithm Team Intern for Summer 2018. This intern will be a highly independent team player who wants to leave a dent in the universe and create tons of value in the process. You will take big responsibilities on the algorithm development side and push the limits of wearable technology through physiological signal processing and data analysis.
QUALIFICATIONS AND REQUIREMENTS
• Physics, Math, Computer Science, or Electrical Engineering Post-doctoral, PhD or Master’s student from a program with rigorous scientific training
• Demonstrable ability to write code in Python
• Coursework on signal processing and linear algebra required. Experience with numerical methods is a plus.
• Strong drive and ability to complete tasks and take responsibility of deliverables
• Active listener
• Strong communication and presentation skills are a must
• Keenly observant and thorough
• Capacity to take work very seriously while not taking oneself seriously – sense of humor is mandatory
• Implement machine learning algorithms using Python
• Implement waveform and spectral analysis algorithms using Python
• Plan and execute experiments to drive data-driven decisions about algorithm development
• Conduct academic literature reviews in fields of physiological signal analysis, machine learning, statistics, experiment design, and signal analysis