Startups · AI

Head of Data Partnerships

83 Sciences · New York, NY, US / San Francisco, CA, US / Remote (US) · Remote

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About 83 Sciences

AI-native materials discovery powered by unpublished experimental data. Backed by Y Combinator.

About the role

Your first job is to build our university data network, starting with individual labs and scaling to departments and institution-wide partnerships. From there, you’ll expand into industry R&D and other major sources of proprietary experimental data.

What they're looking for

  • Build and close partnerships with universities and research labs
  • Navigate complex relationships across faculty, research leadership, tech transfer, legal, and administrators
  • Structure data access, licensing, and partnership agreements
  • Translate partner needs into product requirements
  • Work closely with product and engineering to build tools researchers actually want to use
  • Create scalable partnership models that can grow from one lab to an entire institution
More about this role

83 Sciences is building the data and AI infrastructure for scientific discovery. Almost 90% of experimental data is discarded, losing over $100B in R&D value every year. 83 Sciences turns unused experimental data into the materials and manufacturing processes powering the next industrial revolution.

Our ambition is to own or license the world’s experimental data and use it to build better models for science.

Your first job is to build our university data network, starting with individual labs and scaling to departments and institution-wide partnerships. From there, you’ll expand into industry R&D and other major sources of proprietary experimental data.

  • Build and close partnerships with universities and research labs
  • Navigate complex relationships across faculty, research leadership, tech transfer, legal, and administrators
  • Structure data access, licensing, and partnership agreements
  • Translate partner needs into product requirements
  • Work closely with product and engineering to build tools researchers actually want to use
  • Create scalable partnership models that can grow from one lab to an entire institution
  • Expand the network over time into industry R&D and other...

Read the full posting on 83 Sciences's site ↗

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