Report: Leading Biotech Data Teams
Unlock the Full Potential of Your Biotech Startup’s Data Team with 12 Development Principles.
Imagine your first day leading the data team at a biotech startup. Maybe your title includes “computational biology” or “bioinformatics.” Or maybe it leans more towards “data science” or “machine learning.” Either way, expectations are high: We all know that AI is on its way to revolutionizing drug discovery. At your competitors, it already has. Or at least that’s what your CEO believes. You were hired for this role because you understand the “tech” side. You know how to write software, build predictive models, and turn data into insights. You understand digital technology better than anyone at the company you just joined. But here’s the catch: The biggest problems you’re about to face aren’t technology problems.
To work effectively in a biotech organization, your data team must collaborate with wet lab scientists to understand the potential problems and tools to solve them.
This report presents a cohesive approach to making this partnership work, based on twelve development principles that will help both teams adjust how they work and interact.
In this report, you will learn:
- How to define objectives and priorities that will allow your data team to drive scientific discovery rather than just supporting R&D.
- How to structure communications between data and wet lab teams to ensure effective information flow and coordination.
- How to align and integrate technical development with experiment timelines and workflows to ensure substantive feedback and quick adoption.
About the author
To better understand the nuances of this problem, Jesse has conducted interviews with dozens of biotech leaders in similar roles in other organizations. He developed the insights from these conversations and his own experience into a cohesive and relatively simple theory, which he explores in a weekly newsletter called Scaling Biotech and his latest report, Leading Biotech Teams.
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