Machine Learning Software Developer/Engineer (KTP Associate)

An exciting opportunity has become available for a proactive, highly skilled Masters graduate to lead and deliver the KTP project to develop novel Artificially Intelligent diagnostic and Occupational Health (OH) recommendation tools, using state-of-the-art machine learning techniques including deep learning, kernel machines and natural language processing methods. This position is a 24-month fixed term contract. The associate will also be allocated 10% of their time, and £4k dedicated training budget for the purpose of technical, professional and personal development.

About Heales Ltd
Heales' vision is to provide world class cost effective OH services, minimising absence and maximising staff wellbeing and driving this vision through innovative technology. The successful development of an Artificial Intelligent (AI) diagnostic & recommendation platform will dramatically reduce the cost of services, help resolve the labour shortage in trained nurses and provide an ever-improving service to clients. This automated capability will radically improve offerings, bringing Occupational Health services to new firms and employees.

Website: http://www.heales.com

About KTP
As a KTP Associate you will work with the company to embed new know-how through this innovation project using research and academic support from the University knowledge base. Apply your degree through real work and springboard your career. This position forms part of the Knowledge Transfer Partnership (KTP) funded by Innovate UK. Knowledge Transfer Partnerships is a part government-funded programme to encourage collaboration between businesses and universities in the United Kingdom.

The KTP Associate will be employed by the University of Hertfordshire on a 24-month contract and will carry out duties at the University in Hatfield and the company in Hitchin depending on which aspect of the project is being worked on at the time.

Main duties & responsibilities

• Stage 1: Company & use–case understanding
• Stage 2: Definition of the problem
• Stage 3: Data acquisition
• Stage 4: Solving the problem
• Stage 5: Module build integration and testing
• Stage 6: Field trial
• Stage 7: Business development/commercialisation
• Stage 8: Embedding and disseminating knowledge

Skills and experience needed
You will have experience in machine learning, data mining and platform development, together with how a business functions. Experience of Natural Language Processing, programming applications in C/C++, Java or Python, and using machine learning libraries would be advantageous.
You will understand Project Management in an IT environment, and be able to demonstrate technical skills and understanding in:

• Bayesian data analysis
• Deep learning
• Kernel machines
  You will demonstrate an ability to operate in a dynamic business environment, of being proactive in planning work and of finding solutions to unexpected challenges. You will have good time management skills, the ability to objectively evaluate solutions to problems and have proficiency in English with good verbal and written communication skills, including making presentations and writing professional technical reports suitable for varied audiences.

The successful applicant is likely to demonstrate an interest in economics, psychology, business markets or change management, and also have an ability to reason in a logical way in terms of developing algorithms and creating software to perform these routines.

The successful candidate must have a strong work ethic, be self-motivated and a reliable team player, with an ambition to systematically develop their own skills. Additionally, you will have an interest in the medical world and health care system and in applying technology in different forms. You will be committed to proactive learning and CPD.

Qualifications required
You will hold a good undergraduate degree and a completed MSc in Computer Science or a related subject, with ideally a PhD in Computer Science or a related discipline

Informal Contact Details: Helen Podmore, tel: 01707 286406 email: ktp@herts.ac.uk

Previous applicants need not re-apply

Closing date: 18 October 2020
Interview date: 05-06 November (online) with second round for successful applicants (online or face to face)
Reference Number: 027454
Date advert placed: 17 September 2020

Our vision is to transform lives and UH is committed to Equality, Diversity and Inclusion and building a diverse community. We welcome applications from suitably qualified and eligible candidates regardless of their protected characteristics. We offer a range of employee benefits including generous annual leave and discounted Sports Village memberships, personal and professional development and family friendly-policies. #GoHerts