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10 months ago
Science and Technology Facilities Council
Salary: £30782 - £46576
Location: Didcot
Job type: Permanent
Sector: Software Engineering
Category: Research & Development Jobs
Brief Description

Salary: Data Scientist £30,782 - £36,459 per annum or Senior Data Scientist £38,318 - £46,576 per annum (dependent on expertise and inclusive of sector based RRA)
Grade: UKRI D/E
Contract Type: Open Ended
Hours: Full time or Part Time (Minimum 25 hours per week)
Closing Date: 31st January 2020
Interview Date: February 2020


As a Data Scientist in the SciML group, you will work with the Large-Scale Experimental Facilities at Rutherford Appleton Laboratory and their users to apply state-of-the art AI and Machine Learning methods to translate their data into innovative new science. The SciML Group applies its expertise to real-world data science challenges arising from cutting-edge scientific experiments at the Harwell Facilities. The Group is also a ‘Turing Hub’ – a component of the Alan Turing Institute’s ‘AI for Science’ initiative. The Group runs the PEARL AI computing service, powered by the two, state-of-the-art NVIDIA DGX2 GPU systems, for Turing and STFC researchers working on AI for Science.

The Harwell campus of the Science and Technologies Facilities Council is home to the Rutherford Appleton Laboratory and to the UK research community’s large-scale experimental Facilities. These are the Diamond Synchrotron and Electron Microscopy facilities, the ISIS neutron and muon accelerator, and the Central Laser Facility. Researchers from universities and from industry use these facilities to attack a very wide range of scientific applications ranging from determining protein structures to the physics of materials. In addition, Harwell hosts the NERC Centre for Environmental Data Analysis using the JASMIN Super-Data-Cluster supported by the Scientific Computing Department.

In the last decade, experiments performed at all of the facilities have become much more complex and now generate very large volumes of scientific data. Increasingly, researchers need support and assistance in all aspects of data science, from the generation and acquisition of the datasets on-site at the Facilities, the use of advanced data analytics to extract new science from their data, and through to data curation, management and archiving.

The Software Infrastructure Division of the Scientific Computing Department has been set up to support the different Facility user communities - in respect of simulation and modelling, of scientific software engineering and of all aspects of data science. The Scientific Machine Learning (SciML) group of the Division is focused on the practical application of advanced AI and machine learning technologies to the analysis of the ‘Big Scientific Data’ generated at the Facilities or stored at JASMIN.

Working with real-world problems informs our research directions and the ease-of-use, robustness and transparency of Deep Learning technologies is a major theme. The work of SciML is complementary to the predominantly industry-focused Data Science group at the Hartree Centre on the Daresbury site. With experience gained from working with AI technologies on large datasets, the group will collaborate with the Hartree Centre in providing training and advice not only to university users but also to companies and government.

The post is based at the STFC Rutherford Appleton Laboratory in Oxfordshire.

Applying novel and state-of-the-art machine learning and data analytical techniques to analyse large-scale datasets collected at the Harwell Large-Scale Experimental Facilities and the CEDA/JASMIN Facility, covering the following duties:

* Fostering collaborations with facilities users and scientists to address scientific problems using machine learning techniques
* Assisting in building automated systems underpinned by machine learning models and relevant statistical models, wherever applicable.

Contributing to learning and development at the Rutherford Appleton Lab, and supporting its community by:

* Presenting work internally and externally
* Publishing work in peer-reviewed journals
* Helping researchers to understand the power and limitations of Machine Learning technologies applied to their real-world data
* Assist in running training courses and providing consultancy to both university and industrial users.

For the Senior post the successful candidate will also be expected:

* To play a leading role in the development and evolution of the SciML group
* To assist in embedding best software engineering practices within the solutions created by the SciML group
* To work with partners and stakeholders for ensuring the integration/uptake of the solutions developed by the SciML group
* To assist in developing the strategy for the SciML team within the department.

Contacts and Communication

* Regular contact with staff internally and externally
* Assist in organising and coordinating technical meetings and relevant project events as appropriate
* Maintain effective engagements with stakeholders (e.g. the facilities staff, members of the university research community, the Alan Turing Institute and partners from industry).

Personal Skills and Attributes

* Strong communication skills (verbal, written, and presentation)
* Able to work effectively under pressure, set priorities and make decisions independently
* Able to work with uncertain requirements to develop a consensus on a plan of action.

For the Senior post

* Strong leadership skills
* Ability to delegate effectively
* Proven negotiation and influencing skills
* Proven organizational ability to manage a diverse workload with competing time demands
* High level of self-motivation and drive, and capable of motivating others
* A proven track record of analytical and problem-solving skills.

Any other Relevant Information

* Available to travel/work in the UK and overseas for meetings and events.

Please contact Jeyan Thiyagalingam (t.jeyan@stfc.ac.uk) if you have any queries for this vacancy.

Organization Description

UK Research and Innovation is a new entity that brings together nine partners to create an independent organisation with a strong voice for research and innovation, and a vision to ensure the UK maintains its world-leading position in research and innovation.
The Science and Technology Facilities Council is a world-leading multi-disciplinary science organisation, and our goal is to deliver economic, societal, scientific and international benefits to the UK and its people – and more broadly to the world.

Shortlisting (S) and Interview (I) Criteria


* Educated to degree level or equivalent
* Familiarity with one or more machine learning toolsets (SciKit Learn, TensorFlow, etc.) (S&I)
* Experience managing and organising the parameters and results of computational experiments (S&I)
* Demonstrated ability to assimilate new ideas and turn them into practical, applied techniques and willingness to continue to learn new techniques (S&I)
* Excellent written communication skills as demonstrated by CV and covering letter (S)
* Ability to work both as part of a team, and with a high degree of autonomy (I)
* Excellent verbal communication skills including the ability to present complex or technical information clearly (I)
* Good problem solving abilities (I)
* A high degree of responsibility, commitment and reliability (I)
* Good organisational and project management skills. (I)

For the Band E position in addition:

* A PhD or equivalent professional experience in a field with relevance to data science (S)
* Deep understanding of managing, structuring, and analysing data, including building statistical models and using machine learning technologies (S&I)
* Strong leadership skills with previous experience of/the ability to lead, motivate and develop others.


* Experience in benchmarking processes and automating processes (S&I)
* Experience working with high performance computing, GPU and cloud platforms (S&I)
* Visualisation technologies for understanding large or complex data (S&I)
* Experience as a collaborative team member on complex projects. (S&I)

UKRI supports research in areas that include animal health, agriculture and food security, and bioscience for health which includes research on animals, genetic modification and stem cell research. Whilst you may not have direct involvement in this type of research, you should consider whether this conflicts with your personal values or beliefs.

To enable us to hire the very best people we will conduct a full and comprehensive pre-employment check as an essential part of the recruitment process on all individuals that are offered a position with UKRI. This will include a security check and an extreme organisations affiliation check.

Employee Benefits

UK Research and Innovation recognises and values employees as individuals and aim to provide a pay and rewards package that motivates staff to perform to the best of their ability. The reward package includes a flexible working scheme, a Career Average Revalued Earnings pension scheme, 30 days annual leave allowance and a number of other benefits.

Developing Talent

We are committed to developing employees in their roles throughout their career. Learning and development plans enable employees to continue their professional development through training and development opportunities such as e-learning, classroom training and on-the-job experiences. We encourage our employees to share their learning across teams and organisations.

Equal Opportunities

We strive to make decisions based on individual merit and ability. We welcome applications from all sections of the community and promote equality of opportunity in accordance with the Equality Act 2010. As holders of Disability Confident Employer status, we guarantee to interview all applicants with disabilities who meet the minimum criteria for the vacancy.

Online applications only. Please submit a covering letter and CV ensuring that the IRC reference is included in the filename description of each document uploaded. Please note that failure to address the above criteria or submitted without a covering letter may result in your application not being considered.

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