Head of Data Partnerships

Overview Own the commercial side of data business. Build and close the pipeline of multi-year data licensing contracts with hyperscalers (Anthropic, OpenAI, xAI, Google DeepMind, Meta, Nvidia) and enterprise buyers across all 10 verticals. Founding-level go-to-market role — you define the pricing model, deal structure, and customer strategy the entire data business runs on. First 90 days: sign 2-3 pilot data licensing agreements ($2-10M each, 60-90 day deliverables). First 12 months: convert pilots to $30M-100M+ annual licenses and land at least one anchor foundation partner in the $100M-500M / year range. Onsite at Mountain View lab ≥ 2x per month | Advisor role now → Full-time after funding Qualifications Closed at least three enterprise contracts of $10M+ TCV (total contract value). Ideally at least one $50M+ deal. Provide names of counterparties (where allowed), deal structure, and contract length. Direct experience selling data products, data licensing, AI training data, or data platform services to hyperscaler AI labs (Anthropic, OpenAI, xAI, Google, Meta, Nvidia, Microsoft), major pharma R&D organizations, or DoD / defense primes. Named relationships with procurement or business development contacts at these organizations are a strong plus. Hands-on experience organizing and packaging multi-modal biology datasets in the exact formats hyperscaler AI teams expect — high-content cell imaging (Cell Painting or similar), bulk / single-cell RNA-seq, arrayed or pooled genetic perturbation data (siRNA / CRISPR), and drug response metrics (IC50, dose-response). You must have already delivered a scientific dataset that was consumed directly by an external AI / ML team for model training — meaning you know what schema, format, metadata standard, and delivery mechanism actually gets accepted (not what a paper reviewer accepts). Ideally shipped a dataset at Recursion, Ginkgo, Insitro, Basecamp Research, Certara, HuggingFace scientific team, or a similar organization producing hyperscaler-facing scientific data. Deep understanding of AI training data economics — data licensing structures, exclusive vs non-exclusive contracts, per-sample pricing, API vs bulk delivery, benchmark validation clauses. Ideally experience at Scale AI, Databricks, Snowflake, HuggingFace, Surge AI, Basecamp Research, Recursion, Ginkgo Bioworks, or Nvidia BioNeMo partnerships. Multi-year contract negotiation experience — pricing models, IP assignment, exclusivity terms, indemnification, data provenance clauses, and renewal / expansion mechanics. Scientific literacy sufficient to speak credibly with hyperscaler AI research leads and pharma R&D executives about biology, materials, or defense data — you do not need a PhD, but you must be able to discuss multi-modal data (imaging + omics + perturbation + molecular property) with technical buyers without a translator. Track record of building GTM motion from zero — pricing, positioning, sales collateral, pilot-to-commercial conversion playbook, and hiring the first 3-5 sales / partnerships team members. #J-18808-Ljbffr The University of Texas MD Anderson Cancer Center