リード 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:

• 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:

• 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:

• 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 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 set out how your team should use and adapt these methods
• lead a team through the different phases of the product delivery life cycle
• collaborate with the product manager to influence the direction of work
• identify and involve relevant teams to smoothly deliver data science products into the organisation, ensuring these products inform decision making
• ensure products are monitored, maintained and continually improved, engaging and working with others where necessary
• have oversight of any data science features implemented within products or services

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:

• 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