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Building Strong Data Foundations for Biotech Startups with Jacob Oppenheim

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Manage episode 406215965 series 3526491
Content provided by CorrDyn. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CorrDyn or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This week, we are pleased to welcome Jacob Oppenheim, Entrepreneur in Residence at Digitalis Ventures, a venture capital firm that invests in solutions to complex problems in human and animal health.

Jacob sat down with Ross to discuss the importance of establishing strong data foundations in biotech companies and how to approach the task. We explore the challenges biotech organisations face with existing tools. What are the limitations, and why are current data tools and systems not yet geared toward helping scientists themselves extract meaningful insights from the data?

We also get Jacob’s take on AI in the biotech space and what is needed for it to reach its full potential, plus some of the opportunities new modelling capabilities will allow scientists to explore.

Finally, we looked at the topic of building a team, how to approach this within a start-up, and the role consultancies play in providing expertise and guidance to early-stage biotech companies.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

--

Chapter Markers:

[1:08] Jacob gives a quick overview of his career to date and explains how he landed in his current role at Digitalis Venture and what differentiates it as a venture fund.

[07:42] Ross asks Jacob about the biggest challenges and opportunities facing data scientists, data teams, and start-ups more broadly.

[9:56] Jacob talks about the limitations of existing data management tools within biotech companies.

[13:55] Jacob discusses what is needed as a foundation for AI tools to reach their potential.

[17:12] Jacob argues for the need for a unified data ecosystem and the benefits of a modular approach to tooling.

[23:42] Jacob explains that biology has become more engineering-focused and how this allows data to guide drug development.

[26:14] Ross and Jacob discuss the challenges of integrating data science and biotech teams, including cultural clashes and tooling conflicts.

[32:52] Jacob emphasises the importance of consultancies in the biotech space, particularly for start-ups.

[36:21] Ross asks what the new modelling capabilities are that he is most excited about and how they will drive the industry forward.

[38:45] Jacob shares his advice for scientists and entrepreneurs looking to start a biotech venture and recommends resources.

--

Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

Visit this link: https://connect.corrdyn.com/biotech-ml

  continue reading

25 episodes

Artwork
iconShare
 
Manage episode 406215965 series 3526491
Content provided by CorrDyn. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CorrDyn or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This week, we are pleased to welcome Jacob Oppenheim, Entrepreneur in Residence at Digitalis Ventures, a venture capital firm that invests in solutions to complex problems in human and animal health.

Jacob sat down with Ross to discuss the importance of establishing strong data foundations in biotech companies and how to approach the task. We explore the challenges biotech organisations face with existing tools. What are the limitations, and why are current data tools and systems not yet geared toward helping scientists themselves extract meaningful insights from the data?

We also get Jacob’s take on AI in the biotech space and what is needed for it to reach its full potential, plus some of the opportunities new modelling capabilities will allow scientists to explore.

Finally, we looked at the topic of building a team, how to approach this within a start-up, and the role consultancies play in providing expertise and guidance to early-stage biotech companies.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

--

Chapter Markers:

[1:08] Jacob gives a quick overview of his career to date and explains how he landed in his current role at Digitalis Venture and what differentiates it as a venture fund.

[07:42] Ross asks Jacob about the biggest challenges and opportunities facing data scientists, data teams, and start-ups more broadly.

[9:56] Jacob talks about the limitations of existing data management tools within biotech companies.

[13:55] Jacob discusses what is needed as a foundation for AI tools to reach their potential.

[17:12] Jacob argues for the need for a unified data ecosystem and the benefits of a modular approach to tooling.

[23:42] Jacob explains that biology has become more engineering-focused and how this allows data to guide drug development.

[26:14] Ross and Jacob discuss the challenges of integrating data science and biotech teams, including cultural clashes and tooling conflicts.

[32:52] Jacob emphasises the importance of consultancies in the biotech space, particularly for start-ups.

[36:21] Ross asks what the new modelling capabilities are that he is most excited about and how they will drive the industry forward.

[38:45] Jacob shares his advice for scientists and entrepreneurs looking to start a biotech venture and recommends resources.

--

Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

Visit this link: https://connect.corrdyn.com/biotech-ml

  continue reading

25 episodes

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