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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in experimental data management and integration, with a strong foundation in materials science and hands-on experience in high-throughput experimentation. Proficient in translating scientific requirements into actionable specifications while collaborating effectively with multidisciplinary teams and external partners.
Highest-signal resume keywords
PhD In Materials ScienceHands-On Experience With High-Throughput ExperimentationWorking Knowledge Of PythonExperience With LIMS/ELN SystemsStrong Communication Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Experimental Data ProductionData Quality AssessmentInstrument IntegrationClosed-Loop OptimisationBayesian OptimisationActive LearningDesign Of ExperimentsData HandlingMetadata ManagementProvenance Tracking
Soft Skills
CollaborationCommunicationLeadership
Tools & Technologies
APIsData SchemasResearch Data Management
Industry Keywords
Materials ScienceChemistryPhysicsChemical EngineeringExperimental DisciplineFAIR DataLab Data StandardsMultidisciplinary TeamsPartner LabsAutomation Vendors
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Shape and prioritise the roadmap for bringing experimental data, protocols, and campaigns into the CuspAI platform
- Write requirements, specifications, and acceptance criteria covering experiment types, instrument integrations, and LIMS/ELN connections
- Work with the data team on data models and provenance
- Decide what to build, integrate, or leave to partners’ existing systems
- Lead technical discovery with partner labs by mapping workflows, instruments, sample logic, and data systems
- Own partner-lab onboarding from scoping through the first closed-loop campaign
- Create reusable onboarding patterns and documentation
- Act as the main scientific counterpart for partner labs and represent the product in meetings, workshops, and conferences
- Work with ML researchers to design optimisation campaigns that respect experimental constraints
- Define campaign validation, benchmarks, success metrics, and evidence for partners
- Collaborate with materials scientists, ML researchers, platform engineers, and external partner labs
Requirements
What you’ll need- PhD in Materials Science, Chemistry, Physics, Chemical Engineering or a related experimental discipline, or equivalent experience in scientific research
- Hands-on experience with high-throughput or automated experimentation
- Proven track record translating scientific or process needs into requirements for engineers, software teams, or automation vendors
- Experience validating that built systems work in the lab
- Firsthand understanding of experimental data production, metadata, provenance, instrument quirks, data quality, and machine-learning-ready datasets
- Depth in at least one materials domain, such as semiconductors and thin films, catalysis and electrochemistry, energy materials, polymers and formulations, or separations and surfaces
- Working knowledge of Python for data handling and analysis
- Comfortable working with APIs and data schemas
- Strong communication and collaboration skills with external partner labs and multidisciplinary teams
- Direct experience with closed-loop optimisation, Bayesian optimisation, active learning, or design of experiments is a bonus
- Experience with research data management, LIMS/ELN systems, FAIR data, metadata schemas, or lab data standards is a bonus
- Instrument integration experience is a bonus
- Product or project management experience is a bonus
- Industry or research-institution experience is a bonus
- Experience coordinating work across multiple labs, sites, or countries is a bonus
Benefits
Comp & perks- Equity in CuspAI
- 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays
- 26 weeks parental leave for primary caregivers at full pay
- 12 weeks parental leave for secondary caregivers at full pay
- Professional development budget
- Opportunity to work on meaningful materials science, sustainability, and climate-related problems
- True interdisciplinary teamwork with AI researchers, computational chemists, experimental scientists, and engineers