AI & Data Engineer – KTP Associate – Combined Facilities Management Ltd

Job title:

AI & Data Engineer – KTP Associate – Combined Facilities Management Ltd

Company

Queen’s University Belfast

Job description

Through the Knowledge Transfer Partnership (KTP) Programme, Combined Facilities Management Limited in partnership with Queen’s University Belfast have an exciting and innovative employment opportunity for a dynamic and motivated Computer Science/Data Science/Business Analytics graduate to work on a project to embed advanced predictive analytics, real-time data integration, and AI-driven tools into facilities management to drive operational excellence, agility, and competitiveness.This role is company-based and will be delivered in collaboration with Queen’s Business School.The successful candidate will become part of a team within Combined Facilities Management Ltd (CFM). CFM is the leading provider of multi-trade facilities management services across Northern Ireland. Managing over 80,000 jobs annually, they deliver reactive and planned maintenance, M&E services, and compliance-driven solutions.As part of CFM’s ongoing digital transformation, they have already achieved 90% paperless operations; streamlining workflows and improving efficiency across services. Their diverse workforce includes over 350 internal multi trade/ discipline employees, supported by a supply chain of 1,500 partners, all of whom operate within the company’s digitally integrated systems.However, digital competency across the industry varies, with some teams and subcontractors still facing challenges in adopting new technologies. This KTP project will embed AI-driven predictive analytics, real-time data integration, and process automation to further support frontline workers, enhance efficiency, compliance, and service delivery—while also supporting workforce development to ensure seamless adoption of digital tools.Information about the Company partner can be found at:About the person:The successful candidate must have, and your application should clearly demonstrate that you meet the following criteria:

• Hold or be about to obtain (by July 2025) a minimum 2.1 classification undergraduate degree in DataScience/AI/Computer Science, or similar discipline. Candidates with a 2.2 classification and a Masters-level degree in a relevant subject, or with substantial relevant experience, will also be considered.
• Relevant programming experience in data science languages such as python or R.*
• Relevant experience developing business intelligence solutions using tools such as Microsoft Power BI orsimilar.*
• Relevant experience in predictive analytics and machine learning.*
• Relevant experience in data management, including data storage and integration.*

*may be demonstrated through the completion of a module, student project or placement. Candidates should indicate how their experience can be applied to this post.Please note the above are not an exhaustive list. To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information.A KTP role is the perfect launchpad for your career providing the opportunity to apply your academic knowledge and skills to deliver a strategic innovation project within a company. One of the unique benefits to KTP is access to a substantial development budget and the support and guidance of Queen’s world-class academics and researchers. This role offers an excellent opportunity to work closely between academia and industry whilst developing your skills to run and manage projects.Information about the KTP Programme can be found at:If you are an international applicant and don’t already hold a visa that permits you to take up the role you are applying for, please use the information provided on our website to self-assess whether the University is likely to be able to support a visa application –

Expected salary

£32000 – 38000 per year

Location

Belfast

Job date

Tue, 15 Apr 2025 06:14:00 GMT

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