Valo Health

Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products

San Francisco, CA, US$165,000-$220,000Posted 1 month ago

Job Description

### About Us

Valo Health is a human-centric, AI-enabled biotechnology company working to make new drugs for patients faster. The company’s Opal Computational Platform transforms drug discovery and development through a unique combination of real-world data, AI, human translational models and predictive chemistry.

Our talented team of biologists, chemists and engineers, armed with advanced AI/ML tools, work together to break down traditional R&D silos and accelerate the speed and scale of drug discovery and development.

Valo is committed to hiring diverse talent, prioritizing growth and development, fostering an inclusive environment, and creating opportunities to bring together a group of different experiences, backgrounds, and voices to work together. We embrace new ways of learning, solve complex problems and welcome diverse perspectives that can help us advance patient-centric innovation.

Valo is headquartered in Lexington, MA, with additional offices in New York, NY and Tel Aviv, Israel. To learn more, visit www.valohealth.com.

About the Role...

As a Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products, you will be a core member on a team of data scientists building a powerful computational platform for advancing the discovery and development of new medicines. In this role, you will develop machine learning tools for patient data and drive their adoption across teams, under the guidance of epidemiology and biology program leads. Successful candidates will work with a diverse group of scientists and domain experts, in ways that cut across traditional industry boundaries in an innovative startup environment.

What You’ll Do…

Your primary areas of responsibility will be

* As a senior member of our team, you will lead the development of machine learning (ML) methods and analyses of patient data with diverse stakeholders. For example, integrate clinical insights into supervised and unsupervised learn...

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