Head of Data Partnerships

Head of Data Partnerships

Location: San Francisco / Bay Area | Onsite at Mountain View lab 2 x per month

Role Type: Advisor role now - Full-time upon funding

Compensation: Founding-level equity (Commercialization planned within 1-3 months - final window before valuation increases materially)

About Our Client

Our client generates massive volumes of proprietary scientific data that has never existed before - data that only exists in the physical world, produced by their fast robotic automation wet labs. Every experiment produces multi-modal training data (imaging, omics, molecular property, material characterization, outcome metrics) packaged for direct licensing to hyperscalers and enterprise buyers across 10 key verticals :

Medicine | Energy | Materials | Semiconductors | Magnets | Solar | Nuclear | Hypersonics | Armor | Stealth

Founded by Dan Takahashi , a repeat founder with two prior exits including an IPO.

Role Summary

We are seeking a Head of Data Partnerships to own the commercial side of our client's data business in a founding-level go-to-market (GTM) role. You will define the pricing models, deal structures, and customer strategies that power their entire data revenue stream.

Your mandate is to build and close a high-value pipeline of multi-year data licensing contracts with hyperscalers ( Anthropic, OpenAI, xAI, Google DeepMind, Meta, Nvidia ) and enterprise leaders across all 10 target verticals.

Key Performance Milestones

• First 90 Days: Sign 2-3 pilot data licensing agreements ($2M-$10M each with 60-90 day deliverable windows).
• First 12 Months: Convert pilots to $30M-$100M+ annual licenses and secure at least one anchor foundation partner in the $100M-$500M / year range.

What You'll Be Selling (Day One Proof-of-Concept)

You will lead GTM with a high-impact proof-of-concept dataset from our client's Cancer Drug Validation Workflow (MVP running now) - built as Scale AI-style labeled biology training data for top AI labs.

4 Linked Modalities at Well Level:

1. 8-Channel High-Content Imaging: 3-channel live time-course + 5-channel Cell Painting endpoint.
2. Bulk RNA-Seq: ~32 samples per campaign.
3. 48-Gene Arrayed siRNA Modifier Screen: Causal perturbation data.
4. Efficacy & Toxicity Metrics: IC50, Selectivity Index, Therapeutic Index, % Dead, % Apoptotic.

Our Client's Primary Moat: Our client pairs AI-predicted pharmacodynamics (PD) at the well level directly with wet-lab-measured RNA-seq. Competitors like Tahoe-100M offer wet-lab only, while Recursion and Insitro do not share raw wet-lab data - only our client links both.

You will lead the packaging strategy for hyperscaler consumption on day one, including HuggingFace dataset cards, Parquet / Zarr / WebDataset formats, OME-TIFF / MIAME / MINSEQE metadata, ML benchmark task design, versioning + provenance, and API vs. bulk delivery. (Materials & Defense data products follow in 2027-2028).

Requirements & Qualifications

1. Proven Track Record in Large Enterprise Deals: Closed at least three $1M-$10M+ TCV enterprise contracts (ideally at least one $50M+ deal).
2. Direct Hyperscaler / Defense / Pharma Sales Experience: Direct track record selling data products, AI training data, or platform services to hyperscalers, major pharma R&D teams, or DoD/defense primes. Named relationships with key decision-makers are a major plus.
3. Hands-On Multi-Modal Data Packaging: Experience packaging multi-modal biological datasets (imaging, RNA-seq, perturbation, drug response) into schema and metadata standards accepted and consumed directly by external AI/ML teams.
4. AI Training Data Economics: Deep knowledge of data pricing structures (per-sample, API vs. bulk, exclusive vs. non-exclusive, benchmark validation clauses). Experience at organizations like Scale AI, Databricks, Snowflake, HuggingFace, Surge AI, Basecamp Research, Recursion, Ginkgo Bioworks, or Nvidia BioNeMo partnerships is highly desirable.
5. Complex Multi-Year Deal Structuring: Demonstrated experience negotiating IP assignment, exclusivity terms, indemnification, data provenance, and expansion mechanics.
6. Scientific Literacy: Ability to hold high-credibility technical conversations with hyperscaler AI research leads and pharma R&D executives regarding multi-modal data without needing a translator.
7. 0-to-1 GTM Builder: Experience establishing pricing, sales collateral, pilot-to-commercial playbooks, and hiring the first 3-5 partnership team members from scratch.

Application Screening Questions

Please prepare responses to the following screening questions prior to your interview:

1. Largest Deal Closed: Describe the largest enterprise data or AI infrastructure contract you have closed. Include counterparty (where allowed), TCV, contract length, deal structure (exclusive / non-exclusive / API / bulk / hybrid), and how you sourced + closed it.
2. Hyperscaler Relationships: Describe one named relationship you have at a hyperscaler AI lab (Anthropic, OpenAI, xAI, Google DeepMind, Meta, Nvidia, Microsoft) - role, seniority, prior interactions, and whether you could schedule a meeting with them within 60 days.
3. Data Packaging Experience: Describe a multi-modal scientific dataset you personally packaged and delivered to an external AI/ML team. Detail the prior role, dataset size, exact schema + file format + metadata standard chosen, downstream consumer, and their feedback. Include any experience with paired AI-predicted vs. wet-lab-measured biology data.
4. Pilot-to-Commercial Conversions: Describe one pilot-to-commercial conversion you personally led, detailing initial pilot size, conversion mechanics, resulting annual contract value, and total timeline.
5. Pricing Strategy Understanding: Outline your perspective on AI training data pricing today with a specific example of a data licensing deal structure you have executed or analyzed.
6. High-Resolution Perturbation Data: Describe any direct experience packaging or commercializing high-resolution perturbation datasets (e.g., Perturb-seq, CROP-seq, single-cell RNA-seq, pooled CRISPR screens, or arrayed siRNA). State dataset size, format, and consumer.
7. Logistics & Terms: Confirm your current city of residence, ability to be onsite at Mountain View $\ge 2\times\text$ (plus customer travel), available weekly hours during the advisor phase, hourly rate, and willingness to swap cash for founding-level equity ahead of our commercialization window.