Location: Jubilee Campus
Salary: £35,000 to £42,000 per annum. Plus significant training package, including residential management training accredited by the Chartered Management Institute and a personal development budget of £6,000.
Closing Date: Monday 24 June 2019
Location: Gleeds, Nottingham
The University of Nottingham is inviting applications for a Data Scientist to join our exciting collaboration with Gleeds Cost Management Ltd, a world-class independent property and construction consultancy. Based at Gleeds’ offices in Nottingham, you will have a unique opportunity to lead the delivery of a project to develop advanced data analytics methods to provide structured and risk-modelled cost forecasts of new construction and refurbishment projects.
As an enthusiastic graduate, this is an exciting opportunity for you to manage a Knowledge Transfer Partnership (KTP) and to immerse yourself in a dynamic, high performing, and international construction consultancy. Fully supported by the University, you will be instrumental in delivering a high-profile project within a specific time frame.
A key aspect of your role will be to transfer the latest science-based research in data analysis and machine learning to identify, implement and embed new technologies, practices and processes in to the company for commercial delivery. You will also engage with the company’s customers and a variety of other stakeholders during the project to ensure that the technologies implemented are aligned with Gleeds’ customer requirements. Therefore, an awareness of commercial drivers and previous industrial experience is desirable, but not essential.
You should hold or be close to completion of a post graduate degree or equivalent in data science, artificial intelligence or a closely related discipline.
As a people-orientated individual, you will have the ability to influence and engage collaboratively between the teams at Gleeds and the University of Nottingham. As a proactive person, you will have the enthusiasm and motivation to organise your own time effectively and have a flexible approach to prioritising tasks and work.
Through the KTP, you will be given the support and mentoring you need to nurture your talent. You will have your own budget for training and CPD to help you develop your current skillset and reach your personal and professional career goals.
The University of Nottingham is committed to providing competitive employment packages whilst supporting the well-being of our staff to help them reach their full potential. As a University employee, you will have access to the resources of the University as well as a range of benefits and rewards, including staff discounts and travel schemes and a highly attractive pension scheme.
This is a fixed term, three-year contract funded through a KTP award, a UK Government scheme intended to promote sustained and mutually beneficial relationships between universities and industry. It is hoped that there will be a permanent position available at the end of the project.
Informal enquiries may be addressed to Grazziela Figueredo, email email@example.com. Please note that applications sent directly to this email address will not be accepted.
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Our University has always been a supportive, inclusive, caring and positive community. We warmly welcome those of different cultures, ethnicities and beliefs – indeed this very diversity is vital to our success, it is fundamental to our values and enriches life on campus. We welcome applications from UK, Europe and from across the globe. For more information on the support we offer our international colleagues, visit; https://www.nottingham.ac.uk/jobs/applyingfromoverseas/index2.aspxContinue reading
|Title||Data Scientist - KTP Associate (fixed term)|
|Employer||University of Nottingham|
|Job location||Jubilee Campus, Wollaton Road, NG7 2RD Nottingham|
|Published||May 23, 2019|
|Application deadline||June 24, 2019|
|Job types||Researcher,   Research assistant  |
|Fields||Artificial Intelligence,   Data Mining,   Data Structures,   Databases,   Big Data,   Machine Learning  |