Data Scientist (KTP Associate)

Benefits

• Develop managerial skills and attend two residential managerial workshops (each of one-week duration)
• £4,000 personal training budget
• The opportunity to lead a project, develop project management skills and improve long-term career prospects
• Mentoring by a Knowledge Transfer Network Adviser
• Full access to university resources to complete the project
• Ability to work in a largely self-determined way, across the industry-academic partnership
• Opportunities to develop both technical understanding and commercial awareness
• The possibility of studying for a higher degree or undertaking professional development

The KTP Associate will be an employee of Newcastle University but will spend most of their working time at the company’s premises at One Utility Bill, 5 Media Exchange, Coquet Street and when required at Newcastle University.

The KTP Associate will follow One Utility Bill’s working hours and holiday procedure.

• Your working week will be 40 hours per week, with a shift pattern of Monday- Friday 9:00am-5:00pm.
• Annual Leave applicable to this role is 25 days plus bank holidays.
• Although the contract is fixed term for a duration of 2 years, more than 70% of KTP staff gain a permanent job offer from their KTP company.

For more information or informal enquiries, please contact Prof Hongsheng Dai at hongsheng.dai@newcastle.ac.uk or Prof Kevin Wilson at kevin.wilson@newcastle.ac.uk

Key Tasks

• Anomaly and outlier detection using advanced statistical techniques.
• Develop and apply statistical models (such as seasonal ARIMA models) under a Bayesian framework and machine learning models (such as LSTM), to forecast energy usage at the household level.
• Develop advanced forecasting algorithms by incorporating external variables (e.g. weather, EPC rating), missing data and heterogeneous data patterns
• Translate outputs into pricing strategies
• Disseminate findings through reports, research papers and conferences
• Work with colleagues in One Utility Bill to successfully integrate a range of data sources and developed algorithms into a single platform
• Communicate high level technical concepts to non-specialists

The Person

Knowledge, Skills, and Experience

• Strong theoretical and applied knowledge in time series analysis
• Strong theoretical and applied knowledge in Bayesian inference and computational statistics, including advanced methods such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC)
• Good theoretical and applied knowledge in artificial intelligence and statistical learning models
• Knowledge in data engineering (missing data imputation, outlier detection)
• Excellent experience in R/Python programming

Desirable

• Strong foundation in theoretical statistics, with the ability to apply rigorous statistical principles to complex modelling problems
• Knowledge of optimisation
• Experience of project management, the ability to manage multiple deadlines

Attributes and Behaviour

• Excellent organisational and planning skills
• The ability to work to deadlines
• The ability to work independently and collaboratively with colleagues
• Strong interpersonal skills with the ability to communicate at all levels to different stakeholder groups in both academia and industry
• Good attention to detail
• A natural problem solver
• Excellent verbal, written and presentation skills

Qualifications / Experience

Candidates should have:

• A first degree in statistics, data science, computer science, mathematics or a related discipline
• A higher degree (ideally PhD) in statistics, data science, optimisation or artificial intelligence

One Utility Bill Limited and Newcastle University are offering an exciting opportunity to develop a data driven software model that accurately predicts the energy usage for every residential property in the UK – for use in utility bill management. The modelling will utilise time series analysis and machine learning methods.

Based at One Utility Bill, you will be supported by an interdisciplinary team from Newcastle University, led by Prof. Hongsheng Dai, who has expertise in statistical methodology and statistical applications. You will be also supported by a senior supervisor from One Utility Bill, with expertise in financial control.