Epidemiologist, Google Health
Category : Alumni
About the job
At Google we’re committed to improving the lives of as many people as possible. One of the most important areas in which we’re striving to do that is health.
As an Epidemiologist, you’ll be an effective and influential contributor to our research in the areas of public and environmental health. You will work collaboratively in a team environment to develop products or approaches with potential to improve health at the individual, clinical, or population level. You’ll shape development and evaluation of health-relevant products, lead scientific studies based on these products, and shape the content, design, and execution of products.
Responsibilities
- Use data-driven approaches for the development and evaluation of new health-relevant products, while leading studies, and shaping content.
- Provide epidemiological expertise to relevant Google Product and Research teams, ensuring study design and appropriate epidemiological methods are incorporated into the teams’ work and products.
- Identify advances in the field, and bring them to research and product development.
- Serve as an internal and external advocate for Google Health’s work related to epidemiology and public health.
Minimum qualifications:
- PhD in Epidemiology, Biostatistics, Computer Science, a related field, or equivalent practical experience.
- 2 years of experience in epidemiology research or public health practice.
- Experience leading quantitative health research published in 3 or more peer-reviewed publications.
- Experience in R, SAS, or Python data analysis.
Preferred qualifications:
- Experience in product development, health technology, consumer-focused digital health, and/or experience working with or in government/regulatory agency.
- Experience designing and conducting evaluations in non-randomized settings.
- Experience with methodological and causal inference, and quasi-experimental studies.
- Experience applying machine learning to epidemiological problems.
- Experience working in complex, ambiguous environments with cross-functional teams.
- Ability to directly and independently implement analyses in programming languages (e.g., C++, Java, etc.).
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