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.