MLOps Coffee Sessions #74 with Jesse Johnson, Scaling Biotech.

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// Abstract
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.

// Bio
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.

// Related Links
Jessie's blogposts: scalingbiotech.com

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Jesse on LinkedIn: https://www.linkedin.com/in/jesse-johnson-51619a7/

Timestamps:
[00:00] Introduction to Jesse Johnson
[05:10] Jesse's background
[05:52] Biotech environments
[06:31] Jesse's background in Biotech companies
[09:21] Jesse's journey from academic to software engineering
[12:20] Transition from primary output insights/research into writing code
[14:54] Actual hands-on use case in practice
[19:19] Jesse's career trajectory
[23:57] Where we're at, state-of-the-art data engineering and its outstanding challenges
[26:50] Dewpoint's data and machine learning challenges and tooling
[29:04] Dewpoint's team structure
[30:20] Jesse, the VP of Data Science and Data Engineering
[33:24] New biotech data makes it hard to design a data platform
[35:35] Changes in how biotech data is viewed
[35:54] Experiment data output
[40:19] Solving challenges in structuring real-world context into interpretable data fields
[44:16] Maturity between the current data engineering and MLOps tooling space  
[47:31] Achieving a blogpost mission in 2022
[49:50] Wrap up