- Design and implementation of advanced predictive models applied to diverse data types.
- Integration of state-of-the-art machine learning algorithms into current data models.
- Identification, assessment, and deployment of newly validated techniques.
- Develop visualization, machine learning, optimization and statistical inference solutions using very large scale proprietary and public data sets in health care and life sciences.
- Analyze and model structured data using advanced techniques from statistics, machine learning, data mining.
- Perform exploratory data analysis, generate and test working hypotheses, and uncover critical trends and anomalies.
- Masters degree in math, statistics, computer science, machine learning, or closely related discipline is required.
- Ph.D in similar area highly desireable.
- Strong background in statistics, machine learning, deep learning, graph/network analysis
- Demonstrated expertise in statistical tools (R, Python, SAS) and relevant platforms (Oracle, Hadoop, etc.)
- Demonstrated knowledge of common machine learning approaches ( e.g. , regression, dimensionality reduction, supervised/unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning, autoencoding).
- Solid understanding of data structures and architecture.
- Ability to work independently and take initiative, but also a co-operative team player.