Research Fellow

About Brooklyn Health

Brooklyn Health is solving the measurement problem in mental health through accurate, objective and sensitive measurement of symptoms, enabling precision neuroscience in clinical research and patient care. Through digital phenotyping and AI-powered clinical assessments, the company’s Willis platform is modernizing the technology stack for CNS clinical trials, improving endpoint quality and reducing placebo response. Brooklyn Health’s approach builds trust through open-source methods and scales them through proprietary technology for commercial application. Based in Brooklyn, New York, Brooklyn Health is backed by leading healthcare and technology investors including HealthX Ventures, Metrodora Ventures, Story Ventures, RiverPark Ventures, Laconia Capital, Everywhere Ventures and others. For more information, visit www.brooklyn.health.

About the role

The Research Fellow sits within the Science team, reporting to the Director of Science.

Responsibilities

• Contribute to the development and validation of AI/ML methods for data quality monitoring in clinical trials

• Work alongside other members of the Science team to quantify and measure behavioral manifestations of psychiatric illness from video and audio data

• Work collaboratively with the Engineering team to push developed methods into production

• Document and disseminate findings related to novel methods internally and to both commercial and scientific audiences

Requirements

• The ability to write code i.e. advanced proficiency in data science, particularly using python

• Proficiency with basic software development tools i.e. Github, AWS EC2, S3, SageMaker

• A deep comfort with core concepts in statistics and basic machine learning algorithms

• Pursuing a masters or doctorate in the biomedical sciences (neuro, psych, biomedical engineering, etc.) or computer science

• Familiarity with or an interest in common psychiatric conditions and how they are measured

• The ability to concisely and clearly communicate complex scientific concepts