Statistical techniques are paramount for discovering and acting on new signals. Innovation in this area can lead to better diagnosis, precision treatment selection, novel study designs, and causal attribution. In this role, you'll lead our team of analysts working on problems across multiple data domains. You'll combine strong leadership skills with practical ideas for how to implement our ambitious vision.
The Verily Life Sciences team is focused on helping to move healthcare from reactive to proactive. Combining expertise from the fields of biology, chemistry, physics, medicine, electrical engineering, computer science, we’re developing new technology tools for physicians that can integrate easily into daily life and help transform the detection, prevention, and management of disease. Current projects in development include a smart contact lens with miniaturized glucose sensor; a nanodiagnostics platform to help with early detection of disease; and Liftware utensils for people with tremor.
Grow and manage teams of analysts including career development, technical leadership, collaboration with engineers and setting team priorities.
Drive the development of models for precision medicine.
Qualifications
Minimum qualifications:
Master's Degree in a quantitative discipline (e.g. computational biology, bioinformatics, computer science, statistics, operations research, economics, mathematics or physics) or equivalent practical experience.
Experience in applied statistics in the area of life science and health outcomes research, e.g. use of linear models, multivariate analysis, stochastic models and sampling methods.
Experience in management and career development of analytical teams.
Experience with machine learning techniques.
Preferred qualifications:
PhD in Statistics or other advanced quantitative degree with significant relevant statistics experience.
Ability to articulate business questions and use mathematical techniques to develop an answer using available data. Experience translating analysis results into business recommendations, which includes the ability to communicate complex statistical concepts to non-statisticians.
Experience with healthcare/lifescience data analysis and experiment design.
Experience and background in deep learning.
Experience in clinical study design and statistical analysis planning.
Ability to collaborate with teams to understand the possibilities and limitations of statistical techniques in various settings.
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