データサイエンティスト

You can:

• apply designated quantitative techniques such as time series analysis, optimisation and simulation to create and embed appropriate models for analysis and prediction
• provide guidance on matching data sources with relevant applied mathematics and statistical techniques to meet analysis goals
• apply appropriate statistical techniques to available data to discover new relations and offer insight into research problems, helping to improve organisational processes and support decision making
• access and use the statistical tools available within the organisation

You can:

• work with data engineers and data scientists to design and provide products for the organisation

You can:

• work with stakeholders to identify and address ethical and privacy concerns
• demonstrate and communicate how data ethical issues fit into the wider organisational context
• research developments in data ethics and privacy to improve compliance and processes
• assess and constructively challenge proposed data ethics policies

You can:

• demonstrate practical knowledge of data science tools and techniques
• develop data science solutions that maximise insight
• identify opportunities for how data science can improve data practices

You can:

• show an understanding of the organisation and the benefits of data science
• collaborate to help identify user needs and develop and deliver data science products
• communicate effectively and present analysis and visualisations tailored to your audience

You can:

• manage your CPD (continuous professional development) and can link your learning to objectives and organisational goals
• support data science capability building across the team and wider organisation
• confidently talk about the benefits of data science approaches to existing and potential customers
• demonstrate a good understanding of a range of data science techniques, such as machine learning and natural language processing, and you can use them to build data science solutions, including reports, models and dashboards

You can:

• write moderate to complex programs and scripts
• show a good understanding of testing methodologies and how to deploy code

You can:

• understand the differences between delivery methods, such as Agile and waterfall, and can choose the most appropriate method to deliver each product
• define the minimum viable product (MVP) and support decisions about priorities
• work with specialists in multidisciplinary teams to smoothly deliver data science products into the organisation

できること:

• 自分の仕事が支える臨床・ケアのワークフローを説明する
• 臨床・ケアの同僚と、一般的な医療用語を正しく使う
• 変更が患者のケアにいつ影響しうるかを見分けて伝える

できること:

• データ保護の原則を自分の仕事に適用する
• データ保護影響評価に寄与する
• 情報の請求と記録を正しく扱う

できること:

• 仮名化されたデータを扱い、成果物を公開する前に開示確認を行う
• 連結された、または詳細なデータが、いつ患者を特定しうるかを見分ける

できること:

• 患者集団ごとにモデルの性能を測定し、差があれば報告する
• モデルカードなどで、モデルの想定用途、学習データ、既知の限界を文書化する
• モデルに関する根拠を臨床安全ケースに提供する

できること:

• 誤った、欠けた、または遅れた情報などを通じて、医療 IT システムがどう患者に害を及ぼしうるかを説明する
• 臨床安全上の問題の可能性を、適切な経路で報告する

できること:

• データ項目やフォームに適切なコードを見つけて使う
• 用語ブラウザとリファレンスセットを使う

できること:

• 一部の医療ソフトウェアが医療機器として規制されることを説明する
• 製品が医療機器にあたるかもしれないとき、誰に尋ねればよいかを知っている