We are seeking an enthusiastic and proactive Senior Data Science Specialist to join our dynamic technical operations team, which strives to provide a sector leading technical support for FSE and the wider University. With a commitment to customer service excellence and a passion for science and engineering, the Senior Data Science Specialist will provide an agile technical support service to staff and students to support teaching, research and professional services. FSE's research and teaching quality is recognised globally; this is an exciting opportunity to join us in Manchester and contribute to the provision of a sector leading technical service.
The overall role of the job is to develop required advanced time-series modelling and prediction algorithms or software for SF6 leakage analysis in a project funded through the UKRI Strategic Innovation Fund. In-depth knowledge, experience and programming skills are fundamental in time series models such as ARIMA and machine learning models such as recurrent networks, convolutional neural networks, LSTM and autoencoders. Along with leakage monitoring and modelling, maintenance scheduling is also an important part of the work. Experiences in optimisation and scheduling are also essential, along with programming, time management and inter personal skills.
Responsibilities:
Setting up required data science (DS) machine learning (ML) platform and libraries (e.g. Statsmodels, Scikit Learn, Pytorch, Tensorflow). Data resources and collections (e.g. collecting monitoring and leakage data from collaborators) and data curation and management. Designing and building robust time series models and analysis (developing suitable time series models for SF6 leakage data and predictive maintenance for particular sites and networks). Testing the developed models at various sites and integration for operational tests and validations. Reporting to and interacting or liaising with the University of Manchester team and the entire programme team for project needs.
As this role involves research at a postgraduate level, applicants who are not an EEA national or a national of an exempt country and who will require sponsorship under the Skilled Worker route of the UK Visas and Immigration's (UKVI) Points Based System in order to take up the role, will be required to apply for an Academic Technology Approval Scheme (ATAS) Certificate and will need to obtain this prior to making any official visa application UKVI.
What you will get in return:
Fantastic market leading Pension scheme Excellent employee health and wellbeing services including an Employee Assistance Programme Exceptional starting annual leave entitlement, plus bank holidays Additional paid closure over the Christmas period Local and national discounts at a range of major retailers
As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Our University is positive about flexible working - you can find out more here.
Hybrid working arrangements may be considered.
Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Any CV's submitted by a recruitment agency will be considered a gift.
Enquiries about the vacancy, shortlisting and interviews:
Name: Christopher Page, Technical Operations Manager / Prof. Hujun Yin, Project Lead Email: christopher.page@manchester.ac.uk or hujun.yin@manchester.ac.uk
General enquiries:
Email: People.recruitment@manchester.ac.uk
Technical support:
Jobseeker Support
This vacancy will close for applications at midnight on the closing date. Please see the link below for the Further Particulars document which contains the person specification criteria. #J-18808-Ljbffr
The overall role of the job is to develop required advanced time-series modelling and prediction algorithms or software for SF6 leakage analysis in a project funded through the UKRI Strategic Innovation Fund. In-depth knowledge, experience and programming skills are fundamental in time series models such as ARIMA and machine learning models such as recurrent networks, convolutional neural networks, LSTM and autoencoders. Along with leakage monitoring and modelling, maintenance scheduling is also an important part of the work. Experiences in optimisation and scheduling are also essential, along with programming, time management and inter personal skills.
Responsibilities:
Setting up required data science (DS) machine learning (ML) platform and libraries (e.g. Statsmodels, Scikit Learn, Pytorch, Tensorflow). Data resources and collections (e.g. collecting monitoring and leakage data from collaborators) and data curation and management. Designing and building robust time series models and analysis (developing suitable time series models for SF6 leakage data and predictive maintenance for particular sites and networks). Testing the developed models at various sites and integration for operational tests and validations. Reporting to and interacting or liaising with the University of Manchester team and the entire programme team for project needs.
As this role involves research at a postgraduate level, applicants who are not an EEA national or a national of an exempt country and who will require sponsorship under the Skilled Worker route of the UK Visas and Immigration's (UKVI) Points Based System in order to take up the role, will be required to apply for an Academic Technology Approval Scheme (ATAS) Certificate and will need to obtain this prior to making any official visa application UKVI.
What you will get in return:
Fantastic market leading Pension scheme Excellent employee health and wellbeing services including an Employee Assistance Programme Exceptional starting annual leave entitlement, plus bank holidays Additional paid closure over the Christmas period Local and national discounts at a range of major retailers
As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Our University is positive about flexible working - you can find out more here.
Hybrid working arrangements may be considered.
Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Any CV's submitted by a recruitment agency will be considered a gift.
Enquiries about the vacancy, shortlisting and interviews:
Name: Christopher Page, Technical Operations Manager / Prof. Hujun Yin, Project Lead Email: christopher.page@manchester.ac.uk or hujun.yin@manchester.ac.uk
General enquiries:
Email: People.recruitment@manchester.ac.uk
Technical support:
Jobseeker Support
This vacancy will close for applications at midnight on the closing date. Please see the link below for the Further Particulars document which contains the person specification criteria. #J-18808-Ljbffr
