COMP0058 - Machine Learning Scientist (KTP Associate) - 18 Months FTC

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School of Computing & Engineering
Salary:  

£32,800 to £39,018 per annum

Release Date:  

Monday 15 January 2024

Closing Date:  

Sunday 18 February 2024

Interview Date:  

Friday 01 March 2024

Reference:  

COMP0058

The University of West London (UWL), in partnership with Turing Intelligence Technology Limited (TurinTech), offers an exciting opportunity for a Machine Learning Scientist to work on newly funded Innovate UK Knowledge Transfer Partnerships (KTP) project. This complex and highly technical project requires an academically qualified KTP Associate who has strong theoretical knowledge in machine learning / computing / mathematics, but the project team does not seek previous work experience from the candidate.

Primarily based at TurinTech’s office in central London, the KTP Associate will lead the design and implementation of a smart engine that intelligently discerns the most optimal ensemble machine modelling technique set to improve the efficiency and accuracy of the AI-powered evoML platform commercialised by TurinTech. The evoML platform can automate the entire machine learning model development process, by providing automated functionalities in machine learning code generation and optimisation.

This is a career development opportunity for an enthusiastic KTP Associate to manage a KTP project fully supported by a team of industry and academic experts in machine learning and data science. 

The Role 

In this project, you will work closely with business partner supervisor at TurinTech and the company’s research and engineering teams, to explore ways of incorporating UWL academic team's expertise into the evoML platform in the form of features and functionalities offered by the platform. Ensemble modelling functionalities developed during this project period will become a part of the permanent suit of tools offered by evoML, thereby increasing the commercial potential of the platform. Your main duties will include:

• Researching and understanding the latest ensemble modelling approaches, developments, and challenges
• Developing and implementing ensemble modelling components / features, and ensuring a seamless integration with existing features and functionalities of the evoML production environment
• Conducting advanced functionality and commercial viability testing of the ensemble modelling components / features within the evoML environment 
• Conducting client demonstration, collecting user feedback and assessing the features’ potential to enhance user experience and engagement
• Making and implementing decisions on administrative matters relating to strategy and business development
• Maintaining and nurturing relationships with all stakeholders of the project
• Contributing to the writing of academic publications

The Person

The key requirements of the post in terms of knowledge, skills and experience:

• Advanced degree (Master's or PhD) in machine learning, computer science, statistics, mathematics, or a related subject is strongly preferred. 
• Advanced knowledge in AI and ML and a strong background in statistics
• Knowledge of, or interest in, ensemble modelling
• Proficiency in Python programming, solid grasp of Object-Oriented Programming (OOP) principles and design.
• Experience with version control systems such as Git and GitHub
• Deep knowledge of vital machine learning libraries like numpy, scikit-learn, etc.
• Experience or knowledge of PyTorch or similar deep learning frameworks would be beneficial.

The general skills requires include 

• Excellent written and spoken communication skills  
• Strong organisational skills with a drive to lead and deliver the project
• Ability to build strong alliances with colleagues across the business and the knowledge base
• Awareness and understanding of the commercial drivers are desirable

As a KTP Associate, You will have the passion and desire to organise your own time effectively and you will have a flexible approach to prioritising work tasks and drive transformational change in the world. You will be offered the following benefits:

• A personal training and development budget of £3,000 (exclusive of salary) helping you develop  skillset and reach your personal and professional career goals
• Management training and mentoring by an Innovate UK KTP Adviser
• An interesting and challenging role, with exposure to a variety of stakeholders
• Full access to university resources to complete the project
• Fully supported and mentored to nurture your talent by academic and company supervision

How to Apply 

To apply click on ‘Apply Online’ and fill out the application form. Further information about the application process can be found here: https://jobs.uwl.ac.uk/display.aspx?id=1253&pid=0  

Please email [email protected]   if you need any assistance with the application process.

Interviews are expected to be held on 1st March 2024.

For informal enquiries about the position please contact, Chief Scientific Officer at Turin Tech, Fan Wu via email [email protected]  or Professor Wei Jie, University of West London, via email [email protected] .

Additional Information 

Our school is under-represented in terms of staff from BAME (Black, Asian and minority ethnic) backgrounds, of LGBT+ identities, and with disabilities. UWL is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter, and is a Disability Confident Employer as well as a Diversity Champion for Stonewall, the leading LGBT+ rights organisation. We welcome applications from all sections of the community, particularly those mentioned above to increase diversity in our workforce.

Candidates must be able to demonstrate their eligibility to work in the UK in accordance with the Immigration, Asylum and Nationality Act 2006. 

This position does not meet the University criteria for Skilled Worker sponsorship.

We will intermittently review the applications as part of this open advert, therefore if successful, you will be shortlisted and contacted at any time. 

The University of West London reserves the right to close the role prior to this date should a suitable applicant be found.

Further details: