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Head of Data Partnerships

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Job Description - Head of Data Partnerships

Head of Data Partnerships


Location: SF Bay Area
Type: Full-time


About Radical Numerics


Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast multimodal biological datasets so that biological world models become a reality.


Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools.


About the Role


As the lead of Radical Numerics’ biological data discovery efforts, you will operate at the frontier of science, partnerships, and exploration. Your mission is to identify and unlock the world’s most valuable biological datasets, many of which are not publicly available and require trust, relationships, and persistence to access.


This role is part strategist, part explorer, and part dealmaker. You will work closely with our AI researchers and scientists to identify which datasets will have the greatest impact on training the next generation of biological world models. You will then travel globally to meet researchers, hospital systems, national biobanks, sequencing centers, and pharmaceutical partners face-to-face, building relationships that allow Radical Numerics to access these datasets. 


What You'll Do


Global data discovery



  • Map the global landscape of biological datasets across genomics, epigenomics, transcriptomics, proteomics, metabolomics, imaging, and clinical data.

  • Identify high-value datasets across pharma pipelines, national biobanks, hospital systems, sequencing centers, research institutes, and government repositories.


Relationship building



  • Travel internationally to build trusted relationships with scientists, hospital administrators, sequencing labs, biotech companies, and government programs.

  • Represent Radical Numerics in scientific and industry environments, establishing credibility and trust with institutions that control critical data assets.


Data acquisition strategy



  • Work closely with internal scientists and ML engineers to understand which datasets most improve model capability.

  • Evaluate dataset quality, scale, coverage, and modality diversity to prioritize acquisitions.


Negotiation and dealmaking



  • Structure and negotiate agreements that unlock access to datasets while navigating regulatory, privacy, ethical, and IP considerations.

  • Work alongside legal and compliance teams to close data-sharing and licensing agreements efficiently.


Pipeline development



  • Build and maintain a global pipeline of potential data partnerships.

  • Identify emerging programs, consortia, and initiatives where early engagement could secure long-term data access.


What We're Looking For



  • Strong domain knowledge in genomics, sequencing, and imaging technologies, with intuition for what makes a dataset valuable for training large-scale biological models.

  • Proven ability to build relationships across biotech, pharma, hospitals, research institutes, or global scientific networks.

  • Experience securing access to datasets, partnerships, or collaborations in life sciences or healthcare.

  • Excellent negotiation and communication skills with both scientific and business stakeholders.

  • Comfort operating in ambiguous environments and navigating complex institutions to unlock opportunities.


Most importantly, we are looking for someone who is relentless in pursuing data: someone who will fly across the world to meet the people who control the datasets that will power the future of biological AI.


Nice to Have



  • Prior roles in business development, partnerships, or data licensing in biotech or healthcare.

  • Experience working with international research collaborations or large scientific consortia.

  • Familiarity with regulatory environments around health data and genomics (HIPAA, GDPR, etc.).

  • Prior academic or industry research experience with genomics or multi-omics datasets.

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