Biofourmis is looking for smart and capable Data Scientist on our Data Science team to join our ranks. The ideal candidate should have the passion to use data from biosensors and advanced machine learning techniques for making sense of biosensor data. We are building an end-to-end service that integrates seamlessly into the lives of those patients via multiple touchpoints on front-end while providing intelligent analytics on the backend.
- Research and development of algorithms to detect abnormal subtle changes in physiology using biosensor data in real-time.
- Research and development of algorithms to derive clinical derivative parameters from continuous biosensor data including building disease specific models for patient’s health deterioration.
- Design and architect the entire workflow of the algorithms that includes data inputs, outputs and database storage.
- Optimize data analysis processes and systems for better efficiency and maintenance.
- Conduct epidemiological research to analyze the patterns, causes and effects of health and disease in the cohort of collected patient data.
- Documentation which clearly explains how algorithms have been implemented, verified and validated.
Experience / Training:
- Hands on experience with development of end-to-end data analytics solutions including data exploration/crawling, personalized machine learning model building and performance evaluation.
- Knowledge in big data technologies including cloud computing/distributed computing and data visualization.
- Background in or exposure to healthcare data, human physiology or cardiology is preferred.
- Masters or PhD in Bioinformatics, Statistics, Engineering or related fields with strong statistical modelling and machine learning skills (Bachelors with related working experience may be considered).
- Familiar with clinic pathway or treatment guidelines for cardiovascular disease or oncology. Understanding the fundamentals of physiology/pathology/pharmacology is a plus.
- Proficient with time-series data analysis, anomaly detection, unsupervised learning, hypothesis testing.
- Proficient with programming in Python. Strong Programming in R or C/C++ is a plus.
- Good research ability and critical thinking skills.
- Excellent written and verbal communication skills