データサイエンス責任者

You can:

• identify opportunities to develop statistical insight, reports and models to support organisational objectives, while collaborating across the organisation effectively
• critique statistical analyses
• use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle
• use model outputs to produce evidence and help design services and policies
• understand a broad range of statistical tools, particularly those deployed within the organisation, and can use these appropriately and help others to use them

You can:

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

You can:

• oversee compliance with data ethics standards and legislation across the organisation
• develop, manage and continuously improve an ethical framework within your area
• develop a data ethics culture, influencing others to act on it
• ensure data ethics is applied appropriately when formulating, implementing and evaluating organisational policies and programmes

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:

• champion the role of data science within the organisation
• understand and champion user research, and can design and manage processes to gather and establish user needs
• identify and create opportunities to develop and deliver data science products to support organisational objectives, while collaborating across the organisation to fulfil meaningful goals
• take responsibility for delivering scalable data science products into the organisation, and establishing maintenance support

You can:

• act as a leader or technical specialist, providing detailed support and guidance within the organisation and helping colleagues to develop skills
• set the direction of CPD (continuous professional development) within your team
• keep up to date with new developments in data science and can match those to opportunities in your organisation
• talk confidently about the benefits of data science approaches to existing and potential customers
• demonstrate an in-depth understanding of a wide range of data science techniques, such as machine learning and natural language processing, and detailed knowledge of at least one specialism
• use these techniques to build data science solutions, including reports, models and dashboards

You can:

• write and test scripts and create basic models in one or more languages
• collaborate on shared codebases, using a variety of methodologies

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:

• 保健・ケアのシステムへの深い理解にもとづいて、組織の戦略を形づくる
• 保健・ケアのパートナーやリーダーに対して組織を代表する
• 政策やサービスの変更が、デジタルサービスとケアにどう影響するかを予測する

できること:

• データ保護影響評価と情報共有契約を主導する
• 適法な根拠、同意、秘密保持、保存について、チームに助言する
• インシデントを調査し、改善を勧告する

できること:

• ケアにおける AI を保証し、監視するための組織の基準を定める
• 導入の前後に、高リスクのモデルの保証を主導する
• 安全で公正な AI について、規制当局やパートナーに対して組織を代表する

できること:

• プロダクトや変更のハザード特定とリスク評価を主導する
• ハザードログと臨床安全ケース報告書を書き、維持する
• プロダクトチームとリスクの管理策に合意し、それが機能しているかを確認する
• 臨床リスク管理の基準の適用について、チームに助言する

できること:

• 製品が医療機器かどうか、またその可能性の高いクラスを評価する
• 品質および臨床の同僚とともに、製品の規制対応の作業を計画する

できること:

• 目標を設定し、評価を行いながら、チームをライン管理する
• 心身の健康を支え、勤怠、実績、行為を管理する
• チームの育成と後継者計画を立てる

できること:

• 予算を保有して管理し、差異を予測して説明する
• コストと便益を示したビジネスケースを作成する