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Research Associate, Economic Networks and Transaction Data - Methodological Improvements (ONS Turing Strategic Partnership)

The Alan Turing Institute

Location: London
Salary: £37,000 to £42,000 (depending on experience)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 8th September 2021
Closes: 3rd October 2021

The Alan Turing Institute

The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence whose mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.


The 'Finance and economics' programme brings together leading experts in data science, machine learning, finance and the social sciences, from both academia and industry to tackle the most challenging questions by producing world-leading research with significant impact.

This role will be part of the Economic Networks and Transaction Data project. This project will build understanding of networked data through a combination of data processing for financial transactions data held by the ONS, economic modelling and forecasting using data from the whole economic system, and the development of methods for efficiently reducing the dimensionality and complexity of network-generated data.

The role will focus on ‘Challenge three’. This research challenge seeks to provide methodological improvements for working with network and high-dimensional economic data, in order to provide accurate and informative aggregates for policy decisions and forecasts. When analysing high-dimensional panels of macroeconomic or financial time series state-of-the-art methods tend to suffer from at least two main limitations.


  • Apply state-of-the-art and novel data science and artificial intelligence techniques emerging from the Institute and elsewhere to the business-inspired research challenges of the Turing partnerships:
    • Develop methods for utilising financial transaction data in a national statistics context
    • Contribute to the development of input-output network models, including estimation of these models from disparate data sources. 
    • Develop data-science tools and pipelines to provide economic indicators at various levels of geographic and temporal aggregation, to an industrial standard. 
    • Enable data sharing between organisations and different departments within an organisation.  

Please see our portal for a full breakdown of the Job Description.

Person Specification

  • Research Associate level: PhD in Mathematics, Statistics, Economics, Operations Research, Computer Science or closely related discipline.
  • Research Assistant level: Near completion of a PhD or equivalent level of professional qualification in Mathematics, Statistics, Economics, Operations Research, Computer Science or closely related discipline.
  • A solid background in one or more of the following: Probability Theory, Stochastic Analysis and Control, Bayesian Inference and Probabilistic Machine Learning, mathematical Modelling of collective behaviour of interacting systems and rigorous agent-based modelling.

Please see our portal for a full breakdown of the Job Description.

Terms and Conditions

This full-time post is offered on a two year fixed-term basis with an expected starting date of 1st January 2022. The annual salary is £37,000-£42,000 (depending on experience) plus excellent benefits, including flexible working and family friendly policies,

Application procedure

Please see our jobs portal for full details on how to apply and the interview process.

Equality Diversity and Inclusion

The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly.  In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital or civil partnership status, pregnancy and maternity, religion or belief, sex and sexual orientation.  Reasonable adjustments to the interview process can also be made for any candidates with a disability.

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