Data Scientist for IoT and Machine Learning (KTP Associate)
School of Computing, Engineering & Physical Sciences in partnership with Carfiguano Limited
Full Time: 35 hours per week
Fixed Term: 36 months
An exciting opportunity for a Data Scientist in Internet of Things and Machine Learning has become available to work full time on a 36 month Knowledge Transfer Partnership (KTP) project between Carfiguano Limited and the University of the West of Scotland (UWS).The partnership will address key Water Utility challenges surrounding leakage detection (3.1Bn ltrs lost each day in the UK) by developing a robust and context-aware leak detection and predictive analysis framework supporting both advanced visualisation and GiS integration. This innovative project will represent an industry first when successful, delivering an advanced diagnostic and prognostic system, using existing MEMS-based sensor technology, for UK water utilities by applying machine learning and advanced data analytics expertise available from UWS.
The School of Computing Engineering & Physical Sciences has around 110 academic staff and over 2,300 undergraduate and postgraduate students with sections of the School based at the Paisley, Lanarkshire, Ayr and Dumfries campuses.
The successful candidate will be employed by the University of the West of Scotland but will be based at Carfiguano Limited. The associate will be fully supported by both company staff and staff from the School of Computing, Engineering & Physical Sciences at the UWS. In addition, the associate will receive mentoring support from a local KTP Advisor and will have access to staff from the West of Scotland KTP Centre; providing further support and guidance during your period as a KTP Associate and providing access to an extensive network of KTP projects throughout the UK.
The successful candidate will get the opportunity to register free of charge for a Higher Degree (MPhil or Ph.D.), receive training in Chartered Management Institute (up to level 5), work with senior company management to realise benefits to the business and apply their degree and lead their own project in a business environment.
Carfiguano Ltd (CFG) is an information technology and consultancy business specialising in WiFi engineering and IoT applications. For over a decade the business has delivered professional, reliable and complex WiFi services to the UK's leading WiFi and telecommunications companies such as Sky and EE.
Find out more by visiting: http://www.carfiguano.com/
This position forms part of the Knowledge Transfer Partnership (KTP) funded by Innovate UK. It’s essential you understand how KTP works with business and the University, and the vital role you will play if you successfully secure a KTP Associate position. Visit: http://www.ktp-uk.org/ or contact Stuart McKay firstname.lastname@example.org.
The successful candidate must possess:
A minimum of 2:1 in MSc in Computer Science/Data Science or a relevant degree in the areas of Software Engineering/Development, Information Technology, or Telecommunication.
The ideal candidate will have hand-on experience of design and development of machine learning algorithms, deep neural networks, and deep learning techniques; experience of test-driven development, agile software development, or technical writing is preferred; Hands-on experience in data stream visualization techniques and methodologies; Prior experience of working with machine learning libraries such as Scikit-learn, TensorFlow, Keras, H2o.ai and alike; ability to analyse, design and develop data science solutions within a dynamic business/ academic environment; strong analytical and numerical skills; understanding of agile software development methodologies; including Software documentation, technical writing, preparation, and delivery of user training; experience of systems integration and optimisation challenges and approaches; sound technical knowledge of machine learning, artificial intelligence, and deep learning algorithms and their usage with diverse datasets. Good knowledge and understanding of integration of data analysis and visualization techniques.
If you have questions about this vacancy contact:
Further information, including details of how to apply is available at http://www.uws.ac.uk/about-uws/jobs
Closing date: Thursday 12th December 2019
Interview date: Monday 13th January 2020
UWS is committed to equality and diversity and welcomes applications from underrepresented groups.
UWS is a “Disability Confident” employer.
University of the West of Scotland is a registered Scottish charity, no. SC002520