Posted to IASSIST on: 2016-02-29
Employer: UK Data Service
Employer URL: https://www.ukdataservice.ac.uk/
Big Data Network Support Data Scientist works within the Big Data Network Support directorate of the UK Data Service. This section provides support for working with new and novel forms of data and it coordinate activities, provided expertise and develops best practice. It engages with the wider Big Data investments to ensure that big data and services are comprehensible to the research community. The post holder will be responsible for developing big data models and building out some useful, scalable datasets and systems that can start to be visualised and interrogated for insights by the UK Data Service user community.
The main duties of the post will include:
- Conceive, develop and deliver scalable and sustainable methods, algorithms and approaches that generate insight from social science big data.
- Build a portfolio of deliverables using our Hadoop Distributed File System computing infrastructure both on premises and in the cloud that add value in to the wider Big Data Network.
- Assist with the design and implementation of tools and techniques to enable data visualisation and understanding of Big Data such as smart meter energy data.
- Provide input to wider UK Data Service capacity planning activities regarding new and novel forms of data.
- Assist Functional Director, Big Data Network Support in planning and in the delivery of the core activities of Big Data Network Support including the management of and participation in projects.
- Represent the section at management and other meetings; actively contribute to information exchange within the section and between other sections and service partners as appropriate.
- Manage and participate in projects as appropriate; occasional representation of the team at management and other meetings; actively contribute to information exchange with both internal and external stakeholders
- Playing a coordination role in the UK Data Service Big Data team
- External Training and Capacity Building: Contribute to upskilling external audiences in a range of practices around new and novel forms of data
- Internal Training and Capacity Building: Work closely with colleagues outside of Technical Services to support capacity building and integration of new big data knowledge and activities into the wider team and its working policies and procedures.
- As well as the main duties of the post, the post-holder will be expected to participate in various activities appropriate to their seniority.
- Any other duties required by the Director or his nominee.
- Undergraduate degree (or equivalent qualification) in a computing or related subject
- Experience of work with others to develop, refine and scale data management and analytics procedures, systems, workflows, best practices and other issues.
- Ability and experience dealing with very granular data – preferably from a Hadoop data storage platform
- Experience of both statistical modelling and technical, engineering skills
- Good written and oral communications skills
- Demonstrable time management and prioritisation of work activities
- Can meet the requirements of UK ‘right to work’ legislation*
- Motivated self-starter
- Can fulfil the staff vetting procedure for Government contracts
- Postgraduate degree (or equivalent qualification) in a computing or related subject
- Experience of undertaking data analytics and using the outcomes of those analytics to improve business processes
- Experience of developing solutions based on Apache Hadoop and using the Hadoop Distributed File System or an equivalent
- Experience of using relational databases and management tools (such as Microsoft SQL Server and MS SQL Server Management Studio) in a production environment
- Experience of using statistical analysis packages and languages (such as MATLAB, R, SAS, SPSS)
- Experience of building and maintaining processing pipelines to feed dynamic visualisations of new and novel forms of data
- Ability to create and maintain programming specifications
Archived on: 2016-02-29