- Remote working in the UK
- Contractor post - Inside IR35
- Rates are negotiable - ideally around £600/£700 per day
- Until 31st March 2022
- Deadline: 12 noon 2nd November 2021.
A data scientist identifies complex business problems whilst leveraging data value. They work as part of a multidisciplinary team with data architects, data engineers, analysts and others.
* work with policy and operations teams to understand where data science can add value
* support strategic and operational decision making in order to create impact
* source, access, manipulate and engineer data processes with data that typically have characteristics of volume, velocity and variety, or both
* build credible statistical models from the data and use best coding practices to generate reproducible work
* may draw on other technical and analytical standards from across government and industry
* adhere to the data science ethics framework
* are open minded and demonstrate strong intellectual curiosity
* have an interdisciplinary focus, using techniques and knowledge from across the scientific spectrum
* explore and visualise the data to present the 'story' of the data in a meaningful way, and to
a range of technical and non-technical audiences
* use an evolving range of data analysis tools and techniques, including open source, some
of which must be learnt quickly, as and when required
* continuously seek to expand a range of technical skills in addition to their leadership and
* propagate data science skills in other teams, understanding the variety of functional roles
relating to data science and how they can be most effectively applied to solve business
Data scientists work in an open, transparent and collaborative manner, sharing good practice and
seeking to continuously improve the quality of outputs.
A senior data scientist is an expert data scientist who provides support and guidance to
Senior data scientists:
* are a recognised authority on a number of data science specialisms within
government, with some knowledge of cutting edge techniques
* engage with senior stakeholders and champion the value of data science
* line manage more junior colleagues
* communicate the value of data science to senior stakeholders
Skills and Experience Skills grouping
Applied maths, statistics and scientific practices: Expert
Understands how algorithms are designed, optimised and applied at scale. Can select and use appropriate statistical methods for sampling, distribution assessment, bias and error. Understands problem structuring methods and can evaluate when each method is appropriate. Applies scientific methods through experimental design, exploratory data analysis and hypothesis testing to reach robust conclusions.
Data engineering and manipulation: Expert
Works with other technologists and analysts to integrate and separate data feeds in order to map, produce, transform and test new scalable data products that meet user needs. Has a demonstrable understanding of how to expose data from systems (for example, through APIs), link data from multiple systems and deliver streaming services. Works with other technologists and analysts to understand and make use of different types of data models. Understands and can make use of different data engineering tools for repeatable data processing and is able to compare between different data models. Understands how to build scalable machine learning pipelines and combine feature engineering with optimisation methods to improve the data product performance.
Data science innovation: Expert
Recognises and exploits business opportunities to ensure more efficient and effective ways to use data science. Explores ways of utilising new data science tools and techniques to tackle business and organisational challenges. Demonstrates strong intellectual curiosity with an interdisciplinary approach, drawing on innovation in academia and industry.
Developing data science capability: Practicioner
Continuously develops data science knowledge, utilising multiple sources. Shares data science practices across departments and in industry, promoting professional development and use of best practice across all capabilities identified for data scientists. Focuses on recruitment and induction of data scientists.
Domain expertise: Practitioner
Understands the context of the business, its processes, data and priorities. Applies data
Programming and build (data science): Pratitioner
Uses a range of coding practices to build scalable data products that can be used by strategic or operational users and can be further integrated into business systems. Works with technologists to design, create, test and document these data products. Works in accordance with agreed software development standards, including security, accessibility and version control.
Understanding analysis across the life cycle (data science): Expert
Understands the different phases of product delivery and is able to plan and run the analysis for these. Able to contribute to decision-making throughout the lifecycle. Works in collaboration with user researchers, Developers and other roles throughout the lifecycle. Understands the value of analysis, how to contribute with impact and which data sources, analytical techniques and tools can be used at each point throughout the lifecycle