Staff Quantitative Scientist

Verily•Boston, MA

1d•$130,000 - $196,000

About The Position

As a Staff Quantitative Scientist at Verily you will have the unique opportunity to shape the future of healthcare analytics, tackling complex challenges in personalizing patient care and the development of new approaches to health care delivery. You will be at the forefront of applying cutting-edge statistical and computational techniques to real-world health data, driving innovations that can transform patient outcomes and advance public health.

Requirements

Master’s in Health Economics, Biostatistics, Epidemiology, Data Science, or related field
5+ years in statistical analysis or biostatistics within healthcare.
Proficiency in Python or R for statistical programming.
Experience with health economic and epidemiological methods and study designs.
Qualified applicants must not require employer sponsored work authorization now or in the future for employment in the United States.

Nice To Haves

PhD in Health Economics, Health Services Research, Biostatistics, Epidemiology, Data Science, or related field
Experience with observational healthcare data including claims or electronic health records
Experience with advanced statistical or machine learning methods.
Previous experience working with products and/or digital health solutions.
Strong publication record and experience in study design.

Responsibilities

Design and analyze health studies using statistical and epidemiological methods to support decision-making.
Oversee research projects from design to dissemination, employing scientific rigor by ensuring quality of statistical analysis plans, data, analytical code, and output
Work with Care Delivery, Product, and Commercial teams to demonstrate effectiveness of Verily’s portfolio and develop metrics that enable strategic tracking based on evidence generation
Design and carry out analyses of clinical outcomes, health care costs, and utilization, including KPIs and performance guarantees.
Work with cross functional teams to test service delivery models, and evaluate the impact of new product features and AI on engagement and clinical outcomes.