Associate Data Scientist - ONS - HEO
Government Digital & Data -
ONS operates a flexible hybrid working model across the UK, with colleagues linked to one of our contractual locations in Newport, Titchfield (Fareham), Manchester or Edinburgh and working between office and remote throughout the week.
As part of the hybrid working arrangement there is a 40% minimum office attendance requirement. Attendance is typically at your contractual office, with occasional travel to alternative locations. Due to estates constraints, there are currently temporary exceptions to this for colleagues based at Edinburgh and Manchester who are required to attend the office for a minimum of 20% of their work time.
Job summary
The Office for National Statistics (ONS) is the UK’s largest producer of official statistics, covering a range of key economic, social and demographic topics. These include measuring changes in the value of the UK economy, estimating the size, geographic distribution, and characteristics of the population, and providing indicators of price inflation, employment, earnings, crime, and migration.
The Health, Population and Methods (HPaM) Group are responsible for ensuring ONS continues to produce high-quality analysis and statistics that inform the public on matters of health, population and migration, underpinned by solid methodological practices and quality assurance.
The Climate and Health team will build on ONS’s large-scale social and health data assets and expertise in data linkage, geospatial data and statistical modelling to better understand the current and future impacts of climate change on the health, developing indicators at different geographical scales and exploring inequalities in impact across the population.
Job description
The role is within the Health and International Directorate (HID), on the Climate and Health Project Team.
The Standards for Official Statistics on Climate-Health Interactions (SOSCHI) project aims to advance global measurement of the impacts of climate change on health, helping to address gaps in the knowledge base and support national monitoring and evidence-based policy. The project aims particularly to enhance the role of National Statistics Organisations (NSOs) in the systematic monitoring of climate change outcomes and to help build relevant expertise and production capability in Low and Middle Income Countries (LMIC) statistical institutions. The project’s aims include development of a statistical framework and an online platform to disseminate the indicator metadata, open-source code and interactive calculators.
SOSCHI is a four-year project that has been established and led by the UK Office for National Statistics (ONS), and financially supported by the Wellcome Trust.
This project is being developed jointly with African Institute for Mathematical Sciences (AIMS), Rwanda, and the Regional Institute for Population Studies (RIPS) at the University of Ghana. It will integrate the framework with national contexts and promote long-term capacity development. Our approach aims to be collaborative at all stages, by ensuring realism on the implementation of methods in resource-limited settings.
The project has three main outcomes:
- Develop a transparent and globally generalisable framework for official statistics on climate change, environment, and health, that is explicitly relevant to low- and middle-income countries.
- Develop a global reporting and knowledge-sharing platform and open-source tool set to facilitate high quality research and official statistics. The platform will help to build capacity and reduce the resource burden on low- and middle-income countries of developing climate-health research and statistics.
- Explore statistical methods to better estimate climate-related health risk using real world data sources, including novel and big data, and modelling local impacts. Working with low- and middle-income countries partners, the ONS will generalise models and develop reproducible methodologies, with emphasis on relevance to global environmental issues and exploration of ‘thin’ models for application to data-poor settings. Engagement on geospatial and earth observation data will be a key aspect of this work.
Purpose of the role:
The purpose of the Associate Data Scientist is to code, manipulate, engineer, model and analyse data and/or data processes to underpin Climate and Health outputs. And to be inquisitive, curious about data and keen to learn and develop.
Responsibilities
- Understand the basics of software development and analytical methods, and be able to develop and assure basic models.
- Understand and be able to implement a range of data science techniques, including machine learning.
- Collaborate with others to develop fit for purpose data science solutions and outputs supporting the organisation.
- Prepare and manipulate data and perform analytics, all with appropriate quality assurance.
- Present and communicate effectively.
- Be aware of project delivery methods.
- Be able to create, interpret, and assure statistical outputs effectively.
- Be aware of ethical consideration
- Have knowledge of the technologies used
Person specification
Essential Skills Criteria:
- Programming & Build (data science) - Write and test scripts and create basic models in one or more languages, collaborate on shared codebases, using a variety of methodologies. (Lead Essential Skills Criteria)
- Applied maths, statistics, and scientific practices - 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.
- Data engineering and manipulation - Identify and engage with the appropriate engineering support to design and deliver products into the organisation. Understand the reasons for cleansing and preparing data before including it in data science products, recognise the processes and tools involved, and put reusable processes and checks in place. Access and use different architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation.
- Data Science Innovation - 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.
- Developing Data Science Capability - 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.
- Understanding Product Delivery - 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.
Behaviours
We'll assess you against these behaviours during the selection process:
- Working Together
- Delivering at Pace
- Managing a Quality Service
Technical skills
We'll assess you against these technical skills during the selection process:
- Programming and Build (data science)
- Data engineering and manipulation - assessed via a presentation.
