プリンシパル データサイエンティスト

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:

• help to identify the data engineering requirements for any data science product, while working with data engineers and data scientists to design and deliver those products into the organisation effectively
• understand the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place
• understand a broad range of architectures, including cloud and on-premise, and data manipulation and transformation tools deployed within the organisation, and can use these tools appropriately and help others use them

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:

• be a leader in the data science space
• demonstrate in-depth knowledge of data science tools and techniques, which you can use to solve problems creatively and to create opportunities for your team
• act as a coach, inspiring curiosity and creativity in others
• demonstrate in-depth knowledge of your chosen profession and keep up to date with changes in the industry
• challenge the status quo and always look for ways to improve data science

You can:

• lead and support your organisation area by using data science to create change
• identify opportunities to develop data science products to support organisational objectives, while collaborating across the organisation to fulfil goals
• show an understanding of the role of user research, and can design and manage processes to gather and establish user needs
• communicate relevant and compelling stories effectively and present analysis and data visualisations clearly to get across complex messages
• work with colleagues to implement scalable data science products, and to understand maintenance requirements

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 complex programs and scripts
• seek to make code open source where appropriate
• supervise junior analysts and set coding standards for your team
• understand software architecture and how to write efficient, optimised code
• perform user testing on products prior to launch

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:

• サービスが組織間のケアパスにどう収まるかを分析する
• 臨床医、ケア職員、患者と協力して、デジタルサービスを形づくる
• デジタルに関する決定が、ケア、安全、職員の業務量に与える影響を説明する

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:

• 識別子を分析用データから切り離しておく、仮名化と連結の手法を設計する
• データセットや公表物の再識別リスクを評価し、適切な管理策を選ぶ
• 信頼できる研究環境など、安全な環境についてチームに助言する

できること:

• 臨床の同僚とともに、モデルの検証、バイアス評価、本番での監視を計画する
• モデルをいつ再学習、制限、撤回する必要があるかを判断する
• モデルがいつ医療機器として規制されうるか、どのような根拠が必要かをチームに助言する

できること:

• ハザードのワークショップに参加し、ハザードログに寄与する
• 自分の仕事のために、臨床リスク管理プロセスに従う
• テスト結果など、臨床安全ケースのための根拠を提供する

できること:

• 医療機器の標準を満たすソフトウェアライフサイクルのプロセスに従う
• そのプロセスが必要とする記録を作成する

できること:

• 日々の業務を監督し、フィードバックを与える
• 採用と導入研修に参加する
• 定期的に 1 対 1 の対話を行う