助理数据科学家

You can:

• apply analytical methods including exploratory data analysis and statistical testing to a specific data set, to reach accurate and reliable conclusions
• understand and use different performance and accuracy metrics for model validation in data science projects, hypothesis testing and information retrieval
• compare selected applied mathematics and statistical methods and identify their differences
• access and use the statistical and scientific tools available within the organisation

You can:

• identify the appropriate engineering support to design and provide products for the organisation
• recognise the processes and tools for cleansing and preparing data, and put reusable processes and checks in place
• use different architectures, including cloud and on-premise
• use data manipulation and transformation tools

You can:

• apply fundamental principles of data ethics and privacy in your work under supervision
• share data ethics and privacy risks through appropriate channels

You can:

• adopt an inquisitive and curious approach to data
• seek out and research new data science techniques to support learning
• ask questions to improve your knowledge and learn about data science norms
• see possibilities for improvements and innovation

You can:

• show an understanding of the organisation and the benefits of data science
• collaborate to help identify user needs and develop and deliver data science products
• communicate effectively and present analysis and visualisations tailored to your audience

You can:

• manage your CPD (continuous professional development) and can link your learning to objectives and organisational goals
• confidently talk about the benefits of data science approaches to existing and potential customers
• demonstrate a good understanding of key data science techniques, such as machine learning, and you can use them 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:

• show an awareness of the differences between delivery methods, such as Agile and waterfall, and can propose the most appropriate method to deliver each product
• manage your contribution to tasks to fit in with the work of your wider team

您可以：

• 描述医疗卫生和照护体系的主要组成部分以及组织所支持的服务
• 解释为什么患者安全和保密在您的工作中很重要

您可以：

• 在工作中应用数据保护原则
• 为数据保护影响评估作出贡献
• 正确处理信息请求和记录

您可以：

• 使用假名化数据，并在发布输出前进行披露检查
• 识别关联数据或详细数据何时可能识别出某位患者

您可以：

• 解释模型可能如何伤害患者，例如通过偏差、漂移或过度依赖其输出
• 解释为什么需要在模型将要使用的人群中对其进行验证

您可以：

• 解释临床术语与分类之间的区别
• 识别 SNOMED CT 和 ICD 等常见术语