Científico de datos principal

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

Puede:

• analizar cómo encaja un servicio en las vías asistenciales de distintas organizaciones
• trabajar con profesionales clínicos, personal asistencial y pacientes para dar forma a los servicios digitales
• explicar el efecto de las decisiones digitales en la atención, la seguridad y la carga de trabajo del personal

Puede:

• dirigir evaluaciones de impacto relativas a la protección de datos y acuerdos de intercambio de información
• asesorar a los equipos sobre la base jurídica, el consentimiento, la confidencialidad y la conservación
• investigar incidentes y recomendar mejoras

Puede:

• diseñar métodos de seudonimización y enlace que mantengan los identificadores separados de los datos de análisis
• evaluar el riesgo de reidentificación de un conjunto de datos o una publicación y elegir los controles adecuados
• asesorar a los equipos sobre entornos seguros, como los entornos de investigación de confianza

Puede:

• planificar con colegas clínicos la validación, la evaluación de sesgos y la vigilancia en producción de un modelo
• decidir cuándo un modelo debe reentrenarse, restringirse o retirarse
• asesorar a los equipos sobre cuándo un modelo puede estar regulado como producto sanitario y qué pruebas necesita

Puede:

• participar en talleres de peligros y contribuir a un registro de peligros
• seguir el proceso de gestión del riesgo clínico en su trabajo
• aportar pruebas para un caso de seguridad clínica, como resultados de pruebas

Puede:

• seguir un proceso de ciclo de vida del software que cumpla las normas de productos sanitarios
• producir los registros que exige dicho proceso

Puede:

• supervisar el trabajo diario y dar retroalimentación
• participar en la selección y la acogida
• mantener conversaciones individuales periódicas