主任数据科学家

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

您可以：

• 分析一项服务如何融入跨组织的照护路径
• 与临床医生、照护人员和患者合作塑造数字服务
• 解释数字决策对照护、安全和员工工作量的影响

您可以：

• 领导数据保护影响评估和信息共享协议
• 就合法依据、同意、保密和保存向团队提供建议
• 调查事件并提出改进建议

您可以：

• 设计将标识符与分析数据分开保存的假名化和关联方法
• 评估数据集或出版物的再识别风险，并选择合适的控制措施
• 就可信研究环境等安全环境向团队提供建议

您可以：

• 与临床同事一起规划模型的验证、偏差评估和上线后监测
• 判断模型何时需要重新训练、限制使用或撤回
• 就模型何时可能作为医疗器械受到监管以及需要哪些证据向团队提供建议

您可以：

• 参加危害研讨会并为危害日志作出贡献
• 在您的工作中遵循临床风险管理流程
• 为临床安全案例提供证据，例如测试结果

您可以：

• 遵循符合医疗器械标准的软件生命周期流程
• 编制此类流程所要求的记录

您可以：

• 督导日常工作并给予反馈
• 参与招聘和入职引导
• 定期进行一对一谈话