Head of Data

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering.

We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock.

We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters.

We’re on the cusp of the on-demand materials era. Join us.

Due to rapid company growth and expanding external data partnerships, we are seeking a Head of Data to lead the team and set CuspAI’s data strategy.

This is a rare opportunity to define the data foundation of a frontier AI company, working closely with world-leading AI experts, materials science researchers and partnerships to drive the expansion of our data portfolio and consequent modeling capabilities.

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  Build and own CuspAI's data strategy in close partnership with the leadership team, translating research and commercial priorities into a clear, prioritised view of the data assets we need and the sequence in which we need them.

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  Establish and maintain a rigorous, evidence-led framework to identify high-value data opportunities and architect scalable acquisition or generation pathways.

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  Make and defend build-vs-buy-vs-partner decisions, and be accountable for the outcomes.
  

• Set up and drive initiatives to acquire and build proprietary data assets including commercial licensing, academic and national lab collaborations, targeted experimental campaigns, high-throughput computational generation, and internal lab data generation.

• Scope, stand up and oversee data generation programmes end to end, from experimental design and cost model through to delivery of ML-ready assets.

• Build and maintain a pipeline of prospective data partners across industry, academia, instrument and simulation vendors, and commercial data providers.
  

• Own the relationship with our research leads: understanding their data and codesigning data strategies in their research areas

• Identify, evaluate and propose new data partnerships and deals to our Partnerships team, arriving with a clear thesis on strategic value, data quality, exclusivity, cost and integration effort.

• Collaborate with Finance on the data budget allocating spends against strategic priority and expected return.

• Design and run the data request process - how requests are submitted, triaged, prioritised, resourced and tracked

• Act as the single point of accountability for data commitments made to the research organisation.
  

• Lead, grow and develop the Data team, spanning data acquisition, curation, data architecture and data engineering.

• Set the standards for data quality, provenance and interoperability that the team works to, and hold the bar.

• Represent CuspAI's data work to external partners, research collaborators, and across the company

• Substantial experience owning data strategy or a data acquisition function at a research-intensive organisation - a frontier AI lab, a deep-tech or materials/chemicals/energy company, a national lab, or a research institute - with clear accountability for outcomes rather than execution alone.

• PhD in Chemistry, Physics, Materials Science, Chemical Engineering, Computational Chemistry or a related discipline - or equivalent depth of scientific research experience - with enough technical grounding to interrogate data quality and experimental design first-hand.

• Demonstrated experience originating and shaping external data partnerships: sourcing counterparties, running diligence, and working with commercial and legal colleagues to get deals done.

• Experience managing a meaningful budget, with the commercial judgement to prioritise spend, negotiate terms and justify significant investments.

• Experience leading and growing technical teams, and a genuine appetite for building a team rather than only running one.

• Deep familiarity with the materials and chemical data landscape across experimental and computational domains — for example ICSD, the Cambridge Structural Database, NOMAD, Materials Project — and a realistic understanding of what each is and is not good for.

• Working knowledge of how experimental data is actually produced: lab workflows, instrument outputs, ELN/LIMS systems, unit conventions, incomplete metadata, and what it takes to turn any of it into an ML-ready asset.

• Sufficient technical fluency (Python, SQL, data modelling concepts, ML training data requirements) to work as a peer with data engineers and ML researchers, set direction, and evaluate technical proposals critically.

• Exceptional communicator and relationship-builder
  

• Direct experience with high-throughput experimentation, lab automation, robotics or self-driving lab platforms - specifying them, buying them, or running them.

• Hands-on familiarity with characterisation techniques and their data (XRD, spectroscopy, adsorption isotherms, electron microscopy, device measurements) and with where their failure modes lie.

• Experience with high-throughput computational screening and DFT codes (e.g. VASP, Quantum ESPRESSO) and the economics of generating computational data at scale.

• Experience negotiating data-sharing agreements, data standards or interchange formats, and navigating IP, licensing and confidentiality constraints on scientific data.

• A strong existing network across materials research groups, national labs, instrument vendors or industrial R&D organisations.

• Familiarity with data engineering practice — ETL/ELT, schema validation, data contracts, workflow orchestration — sufficient to hold engineering teams to a high standard.
  

This role could be based in our Singapore (Preferred), Cambridge, London, Amsterdam or Berlin offices, with the expectation of being in the office three days per week. Additionally, there may be regular travel required to other locations for collaboration and project work.