리드 데이터 사이언티스트

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

할 수 있는 일:

• 서비스가 조직 간 돌봄 경로에 어떻게 들어맞는지 분석한다
• 임상의, 돌봄 직원, 환자와 협력하여 디지털 서비스를 형성한다
• 디지털 결정이 돌봄, 안전, 직원 업무량에 미치는 영향을 설명한다

할 수 있는 일:

• 개인정보 영향평가와 정보 공유 협약을 이끈다
• 적법한 근거, 동의, 비밀 유지, 보존에 대해 팀에 조언한다
• 인시던트를 조사하고 개선을 권고한다

할 수 있는 일:

• 식별자를 분석 데이터와 분리해 두는 가명처리와 연결 방법을 설계한다
• 데이터 세트나 공표물의 재식별 위험을 평가하고 적절한 통제를 선택한다
• 신뢰할 수 있는 연구 환경 같은 안전한 환경에 대해 팀에 조언한다

할 수 있는 일:

• 임상 동료와 함께 모델의 검증, 편향 평가, 실제 운영 중 모니터링을 계획한다
• 모델을 재학습하거나, 제한하거나, 철회해야 하는 시점을 판단한다
• 모델이 의료기기로 규제될 수 있는 시점과 필요한 근거에 대해 팀에 조언한다

할 수 있는 일:

• 위험 요소 워크숍에 참여하고 위험 요소 로그에 기여한다
• 자신의 업무에 대해 임상 리스크 관리 프로세스를 따른다
• 테스트 결과 같은 임상 안전 케이스의 근거를 제공한다

할 수 있는 일:

• 의료기기 표준을 충족하는 소프트웨어 생애 주기 프로세스를 따른다
• 그 프로세스가 요구하는 기록을 작성한다

할 수 있는 일:

• 목표를 설정하고 평가를 하며 팀을 직접 관리한다
• 웰빙을 지원하고 근태, 실적, 행위를 관리한다
• 팀의 육성과 승계를 계획한다