- 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.
- PhD in Bioinformatics, Statistics, Engineering or related fields with good statistical modelling and machine learning skills.
- Experience with development of end-to-end data analytics solutions including data exploration/crawling, personalized machine learning model building and performance evaluation.
- Background in healthcare data, human physiology or cardiology is ideal.
- Proficient with time-series data analysis, anomaly detection, unsupervised learning, hypothesis testing.
- Proficient with programming in Python. Good programming in R or C/C++ is a plus.
- Good research ability and critical thinking skills.
Octavius, Yong Whei Jie EA License No.: 02C3423 Personnel Registration No.: R1110096