Principal data scientist

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

You can:

• analyse how a service fits into care pathways across organisations
• work with clinicians, care staff, and patients to shape digital services
• explain the effect of digital decisions on care, safety, and staff workload

You can:

• lead data protection impact assessments and information sharing agreements
• advise teams on lawful basis, consent, confidentiality, and retention
• investigate incidents and recommend improvements

You can:

• design pseudonymisation and linkage methods that keep identifiers separate from analysis data
• assess the re-identification risk of a data set or publication and choose suitable controls
• advise teams on safe settings, such as trusted research environments

You can:

• plan the validation, bias assessment, and live monitoring of a model with clinical colleagues
• decide when a model needs retraining, restricting, or withdrawing
• advise teams on when a model may be regulated as a medical device and what evidence it needs

You can:

• take part in hazard workshops and contribute to a hazard log
• follow the clinical risk management process for your work
• provide evidence for a clinical safety case, such as test results

You can:

• follow a software life cycle process that meets medical device standards
• produce the records that such a process requires

You can:

• supervise day-to-day work and give feedback
• take part in recruitment and induction
• hold regular one-to-one conversations