데이터 사이언티스트
데이터 사이언티스트는 통계, 머신러닝, 그 밖의 고급 기법을 사용하여 보건·돌봄 데이터의 패턴을 찾고, 서비스가 계획하고, 예측하고, 돌봄을 개선하는 데 도움이 되는 모델을 만든다. 대표적인 업무로는 수요 예측, 조기 지원이 도움이 될 환자의 식별, 서비스 변경의 효과 평가가 있다. 임상의, 분석가와 긴밀히 협력하여 모델이 타당하고, 공정하며, 안전하게 쓰이도록 한다.
이 글은 AI 어시스턴트가 영어를 한국어로 번역한 것이며, 한국어 원어민의 검토는 아직 받지 않았습니다. 영국 정부 디지털·데이터 전문직 역량 프레임워크(UK GDaD PCF)와 ESCO의 인용문은 영어 그대로 두었습니다.
- 직군
- 데이터
- UK GDaD PCF 역할
- Data scientist (Data)
- ESCO 직업
- data scientist (ISCO-08 2511); statistician (ISCO-08 2120)
수준
| 밴드 | 역할 수준 | UK GDaD PCF 수준 | 공무원 등급 | 직무평가 점수 |
|---|---|---|---|---|
| 4 | 트레이니 데이터 사이언티스트 | Trainee data scientist | AO/EO | 276 |
| 5 | 어소시에이트 데이터 사이언티스트 | Associate data scientist | EO/HEO | 350 |
| 6 | 데이터 사이언티스트 | Data scientist | HEO/SEO | 427 |
| 7 | 프린시펄 데이터 사이언티스트 | Principal data scientist | SEO/G7 | 507 |
| 8a | 리드 데이터 사이언티스트 | Lead data scientist | G7/G6 | 560 |
| 8b | 데이터 사이언스 책임자 | Head of data science | G7/G6 | 603 |
디지털 헬스 조직에서
- 환자에 관한 의사결정을 뒷받침하는 모델은 오류나 편향이 있으면 해를 끼칠 수 있으므로, 모델은 사용할 집단에서 검증되고 임상 안전이 평가된다.
- 건강 데이터는 돌봄 접근의 불평등을 반영하므로, 환자 집단별 편향을 측정하고 해소하지 않으면 모델이 건강 격차를 되풀이하거나 악화시킬 수 있다.
- 진단이나 치료에 정보를 제공하는 모델 등 일부 모델은 의료기기로 규제될 수 있고 AI 규제도 적용될 수 있다.
- 데이터 사이언스는 연결된 상세한 환자 데이터를 필요로 하는 경우가 많으므로, 작업은 가명처리된 데이터를 사용하여 합의된 정보 거버넌스상의 근거 아래 안전한 환경에서 이루어진다.
- 임상의와 환자는 모델의 출력을 이해하고 신뢰해야 하므로, 설명과 불확실성의 쉬운 소통이 중요하다.
UK GDaD PCF 역할 설명
Data science is a broad and fast-moving field spanning maths, statistics, software engineering and communications. Data scientists will often work as part of a multidisciplinary team, using data and analytics to inform and achieve organisational goals.
In this role, you will:
- be inquisitive
- explore and visualise data
- make recommendations to address complex problems and to inform strategic and operational decision making
- use data ethically and appropriately
- be innovative and adaptable
- explore existing and new data using a range of statistical tools and techniques, such as machine learning and predictive analytics
- find patterns in data and transform them into organisational insight
ESCO 직업과 스킬
ESCO는 각 직업에 대해 고용주가 보통 필요로 하는(필수) 또는 필요로 할 수 있는(선택) 스킬과 지식을 나열합니다. 성장에 관한 대화의 체크리스트로 쓰세요.
data scientist
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
필수 스킬과 지식(63)
스킬과 역량
- apply for research funding
- apply research ethics and scientific integrity principles in research activities
- build recommender systems
- collect ICT data
- communicate with a non-scientific audience
- conduct research across disciplines
- deliver visual presentation of data
- demonstrate disciplinary expertise
- design database scheme
- develop data processing applications
- develop professional network with researchers and scientists
- disseminate results to the scientific community
- draft scientific or academic papers and technical documentation
- establish data processes
- evaluate research activities
- execute analytical mathematical calculations
- handle data samples
- implement data quality processes
- increase the impact of science on policy and society
- integrate gender dimension in research
- interact professionally in research and professional environments
- interpret current data
- manage data collection systems
- manage findable accessible interoperable and reusable data
- manage intellectual property rights
- manage open publications
- manage personal professional development
- manage research data
- mentor individuals
- normalise data
- operate open source software
- perform data cleansing
- perform project management
- perform scientific research
- promote open innovation in research
- promote the participation of citizens in scientific and research activities
- promote the transfer of knowledge
- publish academic research
- report analysis results
- speak different languages
- synthesise information
- think abstractly
- use data processing techniques
- use databases
- write scientific publications
지식
- data engineering
- data ethics
- data mining
- data models
- data science
- data visualisation software
- empirical analysis
- information categorisation
- information extraction
- mathematical modelling
- online analytical processing
- quantitative analysis
- query languages
- resource description framework query language
- scientific literature
- statistical modeling techniques
- statistics
- visual presentation techniques
선택 스킬과 지식(34)
스킬과 역량
- apply blended learning
- create data models
- define data quality criteria
- design database in the cloud
- integrate ICT data
- make data-driven decisions
- manage ICT data architecture
- manage ICT data classification
- manage data
- perform data mining
- teach in academic or vocational contexts
- use spreadsheets software
지식
- Hadoop
- LDAP
- LINQ
- MDX
- N1QL
- SPARQL
- XQuery
- business analytics
- business intelligence
- computational biology
- computer simulation
- data quality assessment
- digital curation
- healthcare analytics
- image recognition
- marketing analytics
- multidisciplinary research
- research design
- scientific computing
- social network analysis
- state estimation
- unstructured data
statistician
Statisticians collect, tabulate, and, most importantly, analyse quantitative information coming from a varied array of fields. They interpret and analyse statistical studies on fields such as health, demographics, finance, business, etc. and advise based on patterns and drawn analysis.
필수 스킬과 지식(50)
스킬과 역량
- apply for research funding
- apply research ethics and scientific integrity principles in research activities
- apply scientific methods
- apply statistical analysis techniques
- communicate with a non-scientific audience
- conduct quantitative research
- conduct research across disciplines
- demonstrate disciplinary expertise
- develop professional network with researchers and scientists
- disseminate results to the scientific community
- draft scientific or academic papers and technical documentation
- evaluate research activities
- execute analytical mathematical calculations
- gather data
- identify statistical patterns
- increase the impact of science on policy and society
- integrate gender dimension in research
- interact professionally in research and professional environments
- manage findable accessible interoperable and reusable data
- manage intellectual property rights
- manage open publications
- manage personal professional development
- manage research data
- mentor individuals
- operate open source software
- perform data analysis
- perform project management
- perform scientific research
- process data
- promote open innovation in research
- promote the participation of citizens in scientific and research activities
- promote the transfer of knowledge
- publish academic research
- speak different languages
- synthesise information
- think abstractly
- write scientific publications
지식
- data ethics
- data quality assessment
- data science
- mathematical modelling
- mathematics
- quantitative analysis
- research design
- scientific literature
- scientific research methodology
- statistical analysis system software
- statistical modeling techniques
- statistics
기타
선택 스킬과 지식(35)
스킬과 역량
- advise on financial matters
- advise on legislative acts
- analyse big data
- apply blended learning
- assist scientific research
- build predictive models
- carry out statistical forecasts
- conduct public surveys
- deliver visual presentation of data
- design questionnaires
- develop financial statistics reports
- develop scientific theories
- develop statistical software
- manage database
- manage quantitative data
- plan research process
- prepare lesson content
- present reports
- provide lesson materials
- teach in academic or vocational contexts
- use mathematical tools and equipment
- use spreadsheets software
- write research proposals
지식
Contains ESCO v1.2.1 data (https://esco.ec.europa.eu/). © European Union. Reuse authorised under Commission Decision 2011/833/EU.