cuspai - Head of Data

• 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. • ADDITIONAL CONSIDERATIONS • 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.