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Scaling Biotech

Posted Jan 17, 2022 | Views 415
# Managing
# Scaling
# Health Care
# Case Study
# Interview
# Dewpoint Therapeutics
# Dewpointx.com
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Jesse Johnson
VP, Data Science and Data Engineering @ Dewpoint Therapeutics

Jesse Johnson is head of Data Science and Data Engineering at Dewpoint Therapeutics, an R&D-stage biotech startup. His interest in exploring complex systems, understanding what makes them tick, then using this understanding to improve and scale them led him from academic mathematics, into software engineering (Google, Verily Life Sciences), and then to Biotech (Sanofi, Cellarity, Dewpoint). His goal is to identify ways to scale biotech research through better software and organizational design.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Vishnu Rachakonda
Data Scientist @ Firsthand

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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SUMMARY

Scaling a biotech research platform requires managing organization complexity - teams, functions, projects - rather than just the traditional volume, velocity, and variety. By examining the processes and experiments that drive the platform, you can focus your work where it matters the most by finding the ideal balance for each type of experiment along with a number of common trade-offs.

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