データサイエンティスト
データサイエンティストは、統計、機械学習、その他の高度な手法を使って、保健・ケアのデータのパターンを見つけ、サービスが計画し、予測し、ケアを改善するのに役立つモデルを構築する。典型的な仕事には、需要の予測、早期の支援から恩恵を受けそうな患者の特定、サービス変更の効果の評価がある。臨床医やアナリストと密接に協力し、モデルが妥当で、公正で、安全に使われるようにする。
このテキストは英語から AI アシスタントによって翻訳されたもので、日本語を母語とする方による確認はまだ行われていません。英国政府デジタル・データ専門職能力フレームワーク(UK GDaD PCF)と ESCO からの引用は英語のままです。
- 職群
- データ
- UK GDaD PCF のロール
- Data scientist (Data)
- ESCO の職業
- data scientist (ISCO-08 2511); statistician (ISCO-08 2120)
レベル
| バンド | ロールのレベル | UK GDaD PCF のレベル | 公務員の等級 | 職務評価ポイント |
|---|---|---|---|---|
| 4 | トレーニー データサイエンティスト | Trainee data scientist | AO/EO | 276 |
| 5 | アソシエイト データサイエンティスト | Associate data scientist | EO/HEO | 350 |
| 6 | データサイエンティスト | Data scientist | HEO/SEO | 427 |
| 7 | プリンシパル データサイエンティスト | Principal data scientist | SEO/G7 | 507 |
| 8a | リード データサイエンティスト | Lead data scientist | G7/G6 | 560 |
| 8b | データサイエンス責任者 | Head of data science | G7/G6 | 603 |
デジタルヘルス組織では
- 患者に関する意思決定を支えるモデルは、誤りやバイアスがあれば害を及ぼしうるため、モデルは使用する集団で検証され、臨床安全が評価される。
- 健康データはケアへのアクセスの不平等を反映しているため、各患者集団のバイアスを測定して対処しない限り、モデルは健康格差を繰り返したり悪化させたりしうる。
- 診断や治療に情報を提供するモデルなど、一部のモデルは医療機器として規制されうるし、AI 規制も適用されうる。
- データサイエンスは、連結された詳細な患者データを必要とすることが多いため、作業は仮名化されたデータを使い、合意された情報ガバナンス上の根拠のもと、安全な環境で行われる。
- 臨床医と患者は、モデルの出力を理解し信頼する必要があるため、説明と、不確実性の平易なコミュニケーションが重要である。
UK GDaD PCF のロールの説明
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 の職業とスキル
ESCO は、各職業について、雇用主が通常必要とする(必須)、または必要とするかもしれない(任意)スキルと知識を列挙しています。育成に関する対話のチェックリストとして使ってください。
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.
必須のスキルと知識(63)
スキルとコンピテンス
- 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
知識
- 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
任意のスキルと知識(34)
スキルとコンピテンス
- 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
知識
- 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.
必須のスキルと知識(50)
スキルとコンピテンス
- 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
知識
- 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
その他
任意のスキルと知識(35)
スキルとコンピテンス
- 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
知識
Contains ESCO v1.2.1 data (https://esco.ec.europa.eu/). © European Union. Reuse authorised under Commission Decision 2011/833/EU.