Data Scientist (KTP Associate)

Organisation
University of Exeter
Reference
VAC-4225
Sector
Energy, Natural Resources & Utilities, IT & Information Services, Scientific Services & Pharmaceuticals
Location
St Austell, Cornwall
Salary Details
£29,515 to £34,189
Job Type
Contract
Closing Date
07/01/2019
This post is available immediately on a 2 year fixed-term basis and will be based at BEST company premises in St Austell to support the work of Professor Richard Everson and Dr Jacqueline Christmas on a Knowledge Transfer Partnership (KTP)

Job Description

This post is available immediately on a 2 year fixed-term basis and will be based at BEST company premises in St Austell to support the work of Professor Richard Everson and Dr Jacqueline Christmas on a Knowledge Transfer Partnership (KTP), which is a partnership between the University of Exeter and Best Energy Saving Technology Ltd. (BEST).  

This project will use machine learning to develop a new energy, building and asset management software product, suitable for use in UK and worldwide markets, with the ultimate aim of improving global energy efficiency. You will research and develop new machine learning algorithms applied to multivariate time-series data.  Work is likely to include the following: machine learning of patterns of "normal" usage; using the learned patterns to identify trends and anomalies; incorporating external data sources, such as weather for more accurate prediction; and separating multivariate signals into their constituent independent components.

You will work closely with both the academic team at University of Exeter and BEST to develop and implement the new energy management system. 

You will be able to:

  • Present information on progress and outcomes
  • Communicate complex information, orally, in writing and electronically
  • Work collaboratively and balance commercial and technical decisions

Applicants must:

  • Possess a relevant PhD (or nearing completion), although applicants with a good first or master's degree will be considered
  • Have a good working knowledge of machine learning and or data science, with experience of software development
  • Knowledge of the practical aspects of energy management systems would be advantageous

Company Description

BEST is an ambitious, dynamic, high-tech energy management Services Company, which provides energy monitoring and energy saving software, hardware and technical consultancy.  Its primary product is Eniscope, a real-time energy management system that disaggregates and processes data on energy consumption and provides real-time and analytic information through graphical displays.  Their distribution partners include Quintex, IBM and NG Bailey, and customers/product end-users include Toyota, Shell, Hilton, KFC and McDonalds.

Based in St Austell, on the southern coast of Cornwall, the company's offices are in the region with the highest natural capital value in the UK, which includes Dartmoor and Exmoor National Parks and some of the UK's best beaches and bathing waters.

Other Application Details

Due to the nature of the role, we will only shortlist candidates who clearly demonstrate their suitability for this role via a covering letter and/or personal statement.

To view the Job Description and Person Specification document please click apply to be redirected to the University of Exeter's  recruitment site. 

For further information please contact David Grinsted (from BEST) on 07538 125380 or david.grinsted@bestenergysaving.com or (from the University) Dr Jacqueline Christmas, 01392 723039 or j.t.christmas@exeter.ac.uk or Prof. Richard Everson 01392 724065 or r.m.everson@exeter.ac.uk.

The University offers some fantastic benefits including 41 days leave per year, options for sector leading policies around maternity, adoption and shared parental leave (up to 26 weeks full pay), paternity leave (up to 6 weeks full pay), and a new Fertility Treatment Policy.

The department has gained a Silver Athena SWAN award as a commitment to providing equality of opportunity and advancing the representation of women in STEM/M subjects: science, technology, engineering, mathematics and medicine.

The University of Exeter is an equal opportunity employer. We are officially recognised as a Disability Confident employer and an Athena Swan accredited institution. Whilst all applicants will be judged on merit alone, we particularly welcome applications from groups currently underrepresented in the workforce. 

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