About the role

About the role

Origin produces matched spatial molecular data, histopathology images and clinical datasets from patient tumour samples. Cancer is well suited to deep learning but lacks data at scale. As R&D comes to depend more on AI systems, the companies building them need data that captures how tumours actually behave in tissue. We start with spatial biology because it captures context that bulk and single-cell data miss: where molecules sit, how cells signal to each other, and how pathways differ across regions of a tumour.

This hire will be our first commercial lead. They will own how Origin’s data reaches pharmaceutical companies, biotech and AI labs, from first conversation to signed licence.

What you’ll work on

• Close Origin’s first data licensing deals with pharma, biotech and AI labs, and turn them into a repeatable pipeline.
• Find the teams inside large pharma and AI labs who own budget and decisions for oncology data, such as translational science, computational biology, AI/ML and external innovation. Build relationships with them.
• Run the full deal process: NDA, data evaluation agreement and license agreement. Structure contracts of $100K and up, including pricing, exclusivity, early access and multi-year terms.
• Package our data for buyers: cohort specs, sample datasets, a data room and the “why this data” story. Work with our computational team so every claim is backed by real results.
• Feed what buyers need (indications, clinical labels, modalities, cohort sizes) back into what we produce next.
• Be the external voice of Origin’s data: at conferences, on LinkedIn and in partner meetings. Keep explaining why oncology R&D needs better data, and show the results our data makes possible.
• Help sell our AI evaluation benchmarks and RL environments (training environments for AI models) to AI labs alongside the data.

Helpful past experience

• Has personally worked on and closed biomedical data licensing deals, ideally in oncology.
• A senior role (Director or similar) in data partnerships, strategic data BD or clinical data solutions at a company like Tempus, Caris, Flatiron, Foundation Medicine, Illumina BioInsight, nference, Truveta or Vi.
• Or: healthcare and life sciences partnerships at an AI data company (Mercor, Scale, Surge, Handshake), or BD at a computational pathology company (PathAI, Aignostics, Paige).
• Knows how large pharma and AI labs buy data, who signs and how long it takes.
• Ability to maximise revenue potential by understanding nuances of the application of interest; then, being able to structure the commercial accordingly. e.g., if application of interest is target id, how does Origin maximise its earning potential if anything of interest comes from the data.
• Was early at a company and helped build its commercial function, or spent many years at one company and now wants a founding role at an earlier-stage company with a clear moat.
• An established network across pharma, biotech and AI, built through deals and conferences.
• Degree in molecular biology or a related field. An advanced degree (MS, PhD or MBA, especially from a program with strong biomedical ties) is a plus.
• Technical enough to talk through spatial transcriptomics, H&E and clinical data with a scientific buyer.

Compensation
$150K–$180K base, 1–2% equity (4-year vest, 1-year cliff), plus a commission on every data licence you close: 5% of first-year contract value, paid on signature. The deal-based upside is uncapped.

About Origin

Origin is producing matched spatial molecular, histopathology imaging, and clinical datasets using cancer tissue samples excised from real patients. We operate our own laboratories, and source tumour samples from leading biobanks.

We believe cancer is a disease ideally suited to be modelled using deep learning, however, we're largely missing the right kind of data at scale. Our initial bet is on spatial biology, helping capture the role of localisation of molecules in 3D, elucidating cell-cell signalling, differentially enriched pathways across regions, and varying cellular composition. This is particularly valuable for validating biomarkers, identifying targets and diagnostics.

As R&D increasingly relies on frontier AI systems, we need to collect real-world evidence at scale to ensure model driven findings translate in the clinic.