5.0 KiB
5.0 KiB
Competences — biometrician
Source: ESCO v1.2.1 occupation-skill relations (http://data.europa.eu/esco/occupation/b0f3f595-4965-4f13-ba5c-f6049b62469f).
Essential
- apply for research funding (skill/competence)
- apply research ethics and scientific integrity principles in research activities (skill/competence)
- apply statistical analysis techniques (skill/competence)
- biometrics (knowledge)
- communicate with a non-scientific audience (skill/competence)
- computational biology (knowledge)
- conduct research across disciplines (skill/competence)
- data science (knowledge)
- demonstrate disciplinary expertise (skill/competence)
- develop professional network with researchers and scientists (skill/competence)
- develop scientific research protocols (skill/competence)
- disseminate results to the scientific community (skill/competence)
- draft scientific or academic papers and technical documentation (skill/competence)
- evaluate research activities (skill/competence)
- execute analytical mathematical calculations (skill/competence)
- increase the impact of science on policy and society (skill/competence)
- integrate gender dimension in research (skill/competence)
- interact professionally in research and professional environments (skill/competence)
- interpret current data (skill/competence)
- life sciences (knowledge)
- manage findable accessible interoperable and reusable data (skill/competence)
- manage intellectual property rights (skill/competence)
- manage open publications (skill/competence)
- manage personal professional development (skill/competence)
- manage research data (skill/competence)
- mathematics (knowledge)
- mentor individuals (skill/competence)
- multidisciplinary research (knowledge)
- operate open source software (skill/competence)
- perform project management (skill/competence)
- perform scientific research (skill/competence)
- plan research process (skill/competence)
- promote open innovation in research (skill/competence)
- promote the participation of citizens in scientific and research activities (skill/competence)
- promote the transfer of knowledge (skill/competence)
- publish academic research (skill/competence)
- research design (knowledge)
- scientific modelling (knowledge)
- scientific research methodology (knowledge)
- speak different languages (skill/competence)
- statistics (knowledge)
- synthesise information (skill/competence)
- think abstractly (skill/competence)
- write scientific publications (skill/competence)
Optional
- advise on legislative acts (skill/competence)
- apply blended learning (skill/competence)
- apply teaching strategies (skill/competence)
- assess environmental impact (skill/competence)
- assist in clinical trials (skill/competence)
- assist scientific research (skill/competence)
- biology (knowledge)
- computational chemistry (knowledge)
- conduct public surveys (skill/competence)
- contribute to development of biometric systems (skill/competence)
- create software design (skill/competence)
- develop scientific theories (skill/competence)
- develop statistical software (skill/competence)
- gather experimental data (skill/competence)
- manage data collection systems (skill/competence)
- manage database (skill/competence)
- prepare exercise session (skill/competence)
- prepare lesson content (skill/competence)
- prepare visual data (skill/competence)
- proteomics (knowledge)
- provide lesson materials (skill/competence)
- SAS language (knowledge)
- screen reader (knowledge)
- statistical analysis system software (knowledge)
- statistical modeling techniques (knowledge)
- stem cells (knowledge)
- teach in academic or vocational contexts (skill/competence)
- write research proposals (skill/competence)
- write work-related reports (skill/competence)
Market evidence (job-ad analysis, 17 ads, as of 2026-07-09)
Share of analyzed job ads mentioning the item (threshold ≥ 20 %). Source: JSearch/Adzuna APIs.
Hard skills
- computer vision — 41 %
- machine learning — 41 %
- deepfake detection — 35 %
- fraud detection — 35 %
- data analysis — 35 %
- data science — 35 %
- agentic system design — 29 %
- biometric systems — 29 %
- multimodal ai — 29 %
- representation learning — 29 %
- biometric analysis — 18 %
Methods
- large-scale experimentation — 35 %
- domain-specific foundation models — 35 %
- embedding-based retrieval — 29 %
- rapid deployment — 24 %
Responsibilities
- team leadership — 53 %
- stakeholder management — 35 %
- fraud detection advancement — 35 %
- customer communication — 35 %
- model development — 29 %
- system architecture — 29 %
- technical leadership — 29 %
- data science vision definition — 18 %
- data science vision ownership — 18 %