Data Scientist Job, Mental Health in South San Francisco, CA or Cambridge, MA


Verily, an Alphabet company, lives at the intersection of technology, data science and healthcare. Its mission is to make the world’s health data useful so that people enjoy longer and healthier lives. They are developing tools and devices to collect, organize and activate health data, and creating interventions to prevent and manage disease.

As a Data Scientist for Verily, you will work cross-functionally with Verily's clinical, software and science teams to create methods for digital quantification of behavior (“digital phenotyping”), in an effort to change the way mental and behavioral health is understood and treated. Digital mental health efforts at Verily include the the examination of various data from mobile sensors and wearable devices. One example includes the AURORA study of post-traumatic stress responses.

You will apply your skills to perform statistical analyses of large, diverse and complex data sets. In this position, you will bring analytical rigor and statistical methods to the challenges of measuring clinical outcomes  and improving mental health care. To do this, you will deliver key analyses for motivating product direction, build reusable analysis tools and work closely with software engineers, product managers and clinicians to deliver user-facing products.

The team combines expertise in biology, chemistry, physics, medicine, engineering, computer science, and more to create interventions that exponentially improve patient care. We partner with leading life sciences, medical device and government organizations to enable fast development, meaningful advances, and deployment at scale. Our work spans many projects, including Project Baseline, the quest to map human health beginning with a 10,000 person observational study; Liftware, stabilizing utensil handles to aid individuals with hand tremor or limited mobility; and Debug, an effort to eradicate mosquito-borne disease with Sterile Insect Technique. For more information, please visit their website.


  • Work with large, complex data sets. Solve difficult, non-routine analysis problems.
  • Develop quality control and pre-processing tools for a broad range of digital data types.
  • Iterate on and build new models of behavioral features from smartphone sensors using a combination of statistical, signal processing and machine learning approaches.
  • Apply advanced statistical methods that relate continuously measured smartphone features to clinical endpoints in a real-world population by applying statistical modeling and machine learning.
  • Communicate highly technical results and methods clearly, as well as, interact cross-functionally with a wide variety of people and teams.


Minimum qualifications:

  • PhD degree in a quantitative discipline (e.g., statistics, computational biology, computer science, applied mathematics, or similar) or equivalent practical experience.
  • Experience with Python or similar.
  • Experience with exploratory and statistical data analysis (such as linear models, multivariate analysis, predictive modeling and stochastic models). Experience extracting and cleaning data sets.

Preferred qualifications:

  • 1 year of relevant work experience (i.e., as a statistician, computational biologist, data scientist), including deep expertise and experience with statistical data analysis.
  • Experience with the analysis of observational clinical data.
  • Familiarity with software engineering practices and experience developing production software.
  • Demonstrated willingness to both teach others and learn new techniques.

How to Apply

For more details and application, see