Data Scientist / Machine Learning Engineer (KTP Associate) - (7611)

Position:Data Scientist / Machine Learning Engineer (KTP Associate) 

Based at:Pumpflow, Manchester 

Salary:Up to £35,000, depending on experience 

The role: 

An exciting opportunity has become available for a recent graduate to work full time 18-month Knowledge Transfer Partnership (KTP) Data Scientist/Machine Learning Engineer position in a project to develop a Machine Learning based solution, combined with timeseries and signal processing techniques, to forecasting dynamic characteristics of centrifugal pumps in large-scale industrial settings. The goal is to use Machine Learning models to support control systems for more sustainable and cost/resource-efficient operation of the equipment. The project is a spin-off of a short-term Greater Manchester AI Foundry Technical Assist Project. It is likely to have a measurable impact in sustainability and to include some key and well-established stakeholders (e.g., UK's Environmental Agency). 

Employed and supported by an academic team from the University, youwill be based at Pumpflow's premises in Manchester. The post is primarily based at Pumpflow's sites, but with possibility of flexible work arrangements. 

To find out more about Pumpflow, go to https://www.pumpflow.net/. 

Qualification we require: 

A first-class BSc in Machine Learning, data science, computer science, artificial intelligence, statistics, mathematics, physics, or a related, computational science discipline. A postgraduate degree and/or substantial related work experience are highly desirable. 

Application requirements: 

• Fluency with a Machine Learning/data science environment and ecosystem such as Python with Jupyter Lab and essential packages like numpy, pandas, matplotlib, and scikit-learn. 
• Experience with processing and analysing tabular, timeseries, and signal data.  
• Good theoretical and practical skills in selection, training, evaluating, diagnosing, deployment, and monitoring of Machine Learning algorithms for classification and regression tasks. 
• Excellent oral and written communication skills; ability to disseminate complex concepts in a clear manner to stakeholders with varying degree of technical and scientific background, as well as to produce publications (e.g., research papers) at international level. 

• Ability to lead a technical project by working both independently and collaboratively, with good decision making and workload/time management skills. 

About KTP: 

For nearly 50 years, Knowledge Transfer Partnerships (KTPs) have been helping to innovate for growth by connecting businesses that have an innovation idea with the academic expertise to help deliver it. Currently around 800 businesses, 100 knowledge bases and over 800 graduates are involved in KTPs – collaborative, transformative three-way partnerships creating positive impact and driving innovation. 

Benefits: 

• £2,000 per year to spend on personal training. 
• Prospect of research publications at reputable venues and with societal impact, which can lead to further funding and spin-off projects. 
• Opportunity to register on a higher degree at a reduced cost. 
• Opportunity of a permanent position with the company: 70% of host companies make a permanent job offer to their Associate at the end of the project. 

For an informal discussion, please contact Dr Huw Lloyd huw.lloyd@mmu.ac.ukor Luciano Gerber l.gerber@mmu.ac.uk. 

Apply at https://manmetjobs.mmu.ac.uk/jobsby submitting a CV and a covering letterdetailing how you meet the criteria for the role. Only applications with a covering letter will be considered. 

Due to the nature of KTP funding, those who have already completed more than 1 year of a KTP are not eligible to apply.

Manchester Met University is committed to creating an intentionally inclusive culture of belonging that promotes equity and celebrates diversity. We understand the importance of having a diverse workforce and the benefits it can bring to ensuring diversity of thought and innovation in everything we do. We, therefore, encourage applications from our local and international communities, in particular people from ethnic minority groups, disabled people and people who identify as LGBTQIA+.