The Computational Biologist will report directly to the company’s Chief Scientist. The Computational Biologist will work with the Chief Scientist to execute projects that investigate the viability of a range of biologically inspired computational methods on data forthcoming from pilot initiatives towards achieving the company’s strategic objectives. The projects will range from extracting relevant physiological, behavioral and environmental data streams from a range of data acquisition sources towards building integrative models that encapsulate current working knowledge on how human behavior, physiology, health risk etc. are interrelated. This position requires a strong drive to develop a quantitative understanding of the latest developments in public health studies and published models, and to translate this knowledge into innovative algorithms and research outputs.
- Follow proper scientific methodology in development of advanced integrative mathematical models.
- Conduct research & clearly capture/document information to promote knowledge transfer & understanding of the research objective.
- Contribute to the specification of a model and ensure that all assumptions & limitations are clearly communicated to all relevant parties & that it is captured in the required documentation.
- Follow scientifically sound methodology to validate new algorithms or models (including, but not limited to: test assumption & limitations, check model assumptions, assessing bias and variance of the model, etc.).
- Software development
- Create & maintain any code produced for a model, algorithm, or method of data analysis on the LifeQ repository & it is expected that best practices related to any type of coding should be followed.
- Competence development
- Continuously improve relevant skills & acquire multi-disciplinary knowledge to enable teamwork & co-development with various teams within the company.
- A candidate with life and/or medical science training will carry extra weight in the application
- A background in Physics and/or Applied Mathematics will carry extra weight in the application
- Additional skills and experience in ordinary differential equation based modelling will carry extra weight in the application
- Additional skills in Biometrics and understanding of concepts arising in public health studies will carry extra weight in the application
- Additional skills in analyzing time series data, digital signal processing (frequency domain analysis) will carry extra weight in the application
- Any experience in data science related work (feature engineering, modeling, predictive analytics, etc.) will carry additional weight in the application
- Being comfortable with machine learning techniques (Random Forests, Neural Networks (ANN, Recurrent NN, Deep Learning), Support Vector Machines) will carry additional weight in the application
- General programming experience, but specifically in R or Python (preferred) & use of version control (managing Git repositories)
- Additional experience in connecting to external API data resources, such as those hosting digital health data will carry extra weight in the application