Company
 
Cardiosense is a medical technology startup that operates at the
intersection of wearable technology and artificial intelligence to improve
patient health. Our mission is to use physiological data to predict cardiac
illness and enable early interventions so people can enjoy healthier, longer
lives.
 
Our team brings together experts in data science, electronics, and
healthcare and has partnered with leading healthcare and academic
institutions to introduce the next generation of patient monitoring
solutions.

Description

As a Data Scientist, you will play a role in:

  • Pre-processing and feature extraction of raw time-series physiological data. 
  • Utilizing classical machine learning and deep learning techniques to develop predictive models for determining clinically relevant outputs. 
  • Developing scalable data ingestion and model training pipelines.
  • Deploying production-level code in a cloud-based environment.
  • Presentation of data and key metrics to stakeholders.

Responsibilities

  • Collaborate with and support analysis needs of multidisciplinary teams to help develop/advance our core technology.
  • Read literature to implement cutting edge algorithms.
  • Present written and oral reports and proposals to peers and management, and play a core role in shaping and productionizing new products and cutting-edge applications.
  • Write production-ready code that is version controlled, readable, efficient, and well-tested.

Qualifications

  • BS in Electrical Engineering, Computer Engineering, Computer Science or related field. 
  • 3+ years experience in signal processing theory, biosignal analysis, machine learning or related areas of time series analysis.
  • Experience with wearable health sensors and related biometric algorithms, including ECG, PPG, and respiratory signals. 
  • Familiarity with both classical biostatistics and modern machine learning/AI concepts.
  • Proficiency with Python and popular data analysis packages (Pandas, Numpy, Scikit Learn, Tensorflow/Pytorch etc.).
  • Familiarity with software development lifecycles (SDLC) and experience developing production-grade software
  • Willingness to teach others and learn new techniques.

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