Head of Data Partnerships

We use AI to mine discarded experimental data and drive scientific breakthroughs. Working alongside labs, we discover the materials that will power the new Industrial Revolution.

90% of experiments never make it to publication. Failed runs, abandoned hypotheses, and routine characterization data live in scientists' heads and scattered notebooks. 83 Sciences captures that hidden data at the source, structures it into a queryable "lab brain," and puts it to work: helping researchers learn from their lab's full history and discover new materials.

We are working hands-on with our first cohort of university lab partners and we're backed by Y Combinator (S26). Founded by scientists who lived the file drawer problem firsthand — we're a small team where everyone ships product and talks to researchers directly.

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.

You will:

• 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 proprietary data sources

We’re looking for someone who can operate like a founder: build relationships from scratch, manage ambiguity, move quickly, and turn partner insight directly into product and strategy.

Your mandate: build the network that gives 83 Sciences access to the world’s most valuable experimental data.

Our platform, Dalton, turns messy, heterogeneous lab data into structured, queryable knowledge. The technical problems we're working on include:

Multimodal data capture: OCR pipelines that convert handwritten lab notebook pages into structured entries and speech-to-text tools

Scientific data analysis: automated PXRD/characterization analysis: phase prediction, peak identification, anomaly detection

Chemistry-specific ML: models tuned on a lab's full experimental history (including failed runs) to predict synthesizability and reaction outcomes and suggest optimal process conditions

You'll work across the full stack (data pipelines, ML, and product) with real lab data and direct feedback from working scientists.