Data Scientist
Company
Cardiosense is a digital health company that operates at the intersection of wearable technology and artificial intelligence to improve patient health. Our mission is to use physiological waveform data to predict cardiac illness and enable early interventions so people can enjoy healthier, longer lives.
To achieve our mission we are building a physiological waveform AI platform to develop predictive biomarkers to detect and manage cardiac disease. The company has developed a suite of novel digital tools, multi-sensor devices, and analysis algorithms for use by care providers to detect clinical worsening earlier, inform personalized therapy, and improve patient outcomes.
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 waveform monitoring and analytics 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.