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Digital / data / automationDigital / data / automation
Data Economist
Data economists study numbers and statistics to understand how the economy works and what decisions businesses and governments should make. They help people understand what information tells us, and why it matters.
No degree needed for many routes
AI impact: high£££ payDirect entry route
62
AI impact
how much AI is reshaping it
Robin · your guide
Curious about being a data economist? Here's the honest picture - what you'd really do, what you'd earn, and every way in. No need to decide anything yet.
What you'd actually do
As a Data Economist, you work with big sets of numbers to answer questions about the economy. You gather information from lots of sources, clean it up, and look for patterns that show how things really work - whether that is how people spend money, why some industries grow faster, or whether a new government policy will help people.
Most of your time is spent using computer software to sort and study these large datasets. You build models - which are like experiments on numbers - to test ideas and make predictions. You then explain what you found to people who make decisions, like business leaders or government officials, and make sure they understand what the numbers really mean. You have to stay current with new statistical methods and new sources of data.
1Collect, clean, and analyze large datasets to extract meaningful economic insights.
2Develop econometric models to forecast economic trends and assess policy impacts.
3Collaborate with cross-functional teams to integrate data findings into strategic decisions.
4Present analytical findings and recommendations to stakeholders in a clear and compelling manner.
5Stay updated on economic theories and data analytics techniques to enhance analytical approaches.
6Utilize advanced statistical software and programming languages to manipulate data efficiently.
7Conduct research on current economic conditions and emerging market trends.
Career progression & pay
01
Getting in
Junior Data Economist
£28,000 - £34,000
BSc in Economics, Data Science, or related field
As a Junior Data Economist, you will assist in data collection and preliminary analyses, gaining hands-on experience with statistical tools and methodologies.
02
Building up
Mid-level Data Economist
£40,000 - £50,000
3-5 years experience in data analysis or economics, proficiency in R or Python
In this role, you will lead projects, conduct complex analyses, and present findings to senior management, playing a key role in strategic decision-making.
03
At the top
Senior Data Economist
£60,000+
10+ years experience, chartered status with RES or equivalent
As a Senior Data Economist, you will oversee major projects, mentor junior staff, and influence policy decisions at a high level, establishing yourself as a thought leader in the field.
Degrees that lead here via Economics
Accounting and Economics
University of Southampton
Accounting and Finance / Economics
Prifysgol Aberystwyth
Arabic and Economics
University of St Andrews
Archaeology/Business Economics
University of Glasgow
Archaeology/Economic & Social History
University of Glasgow
Biblical Studies and Economics
University of St Andrews
Apprenticeships that lead here
Professional economist (integrated degree)
Legal, finance and accounting
Level 6 · Degree3.5 yrs
Senior professional economist (integrated degree)
Legal, finance and accounting
Level 7 · Master's2 yrs
Software and data foundation apprenticeship
Digital
Level 2 · GCSE level0.7 yrs
Data technician
Digital
Level 3 · A-level2 yrs
Data analyst
Digital
Level 4 · Higher2 yrs
Data protection and information governance practitioner
Business and administration
Level 4 · Higher1.5 yrs
Who hires - top UK employers
Office for National Statistics
The ONS is the UK's largest independent producer of official statistics, offering Data Economists the chance to work on national economic data and policy.
Bank of England
As the central bank of the UK, the Bank of England provides opportunities for Data Economists to influence monetary policy and financial stability.
HM Treasury
Working at HM Treasury allows Data Economists to engage directly with economic policy-making and fiscal strategy.
Deloitte
Deloitte offers a dynamic environment for Data Economists to work on economic consulting projects across various sectors.
PwC
PwC provides Data Economists with opportunities to analyse economic data for clients in diverse industries, shaping business strategies.
AI & the future of this job
Data economists sit at a genuinely contested intersection: AI is exceptionally good at the data wrangling, model execution, and pattern recognition that fill a large chunk of this role's day-to-day work. LLMs can now draft econometric summaries, run regression pipelines, and surface anomalies faster than any junior analyst. What AI cannot yet replace is the contextual economic judgement required to ask the right question of the data, challenge a model's assumptions, or translate findings into policy recommendations that account for political and social realities. The role survives, but its shape is changing quickly and the entry-level pipeline is already narrowing.
Within 5 Years
Significant workflow disruption
By 2031, AI coding agents and automated data pipelines will handle the bulk of data collection, cleaning, and standard econometric modelling that currently occupies junior data economists. Employers will expect graduates to arrive already comfortable directing these tools rather than performing the underlying tasks manually. Headcount at the entry level will likely contract in financial services and large consultancies, though public sector roles in the ONS, HM Treasury, and local government may prove more stable. Graduates who can operate as 'economist plus AI director' rather than 'economist who also codes' will be best positioned.
Within 10 Years
Role significantly redefined
By 2036, the data economist role as currently described will have split into two distinct tracks: a smaller, senior track requiring deep causal and policy expertise where human judgement is genuinely irreplaceable, and a broader applied analytics track where the job is increasingly about managing AI-generated outputs and communicating them credibly to non-technical stakeholders. Salaries at the top end should remain strong precisely because the pool of people with genuine economic intuition will not grow as fast as demand for economic insight does. Mid-tier roles, however, face sustained pressure as AI systems become capable of end-to-end analysis on well-defined problems.
Within 20 Years
Deeply transformed, smaller field
By 2046, it is plausible that near-fully automated economic analysis systems handle routine forecasting, policy impact assessment, and market monitoring with minimal human input. The data economist who survives in that landscape will be closer to a scientific director or institutional economist: someone who governs the questions asked, audits model assumptions, and takes responsibility for the societal consequences of data-driven decisions. The profession will be smaller but more prestigious and better paid, similar to how actuarial science contracted and professionalised simultaneously. Students entering today should build towards that senior, judgement-heavy tier from day one rather than expecting a traditional career ladder to carry them there.
How to stay ahead
Master causal inference, not just correlation
Predictive modelling is the part of this job AI will absorb fastest. Double down on causal inference methods, natural experiments, and quasi-experimental design, because determining whether a policy actually caused an outcome requires the kind of economic reasoning AI still handles poorly. Programmes at UCL, LSE, and Edinburgh that include dedicated causal econometrics modules are worth prioritising for precisely this reason.
Develop a policy communication specialism
The ability to translate complex findings into decisions that policymakers, boards, or the public will actually act on is underrated and undervalued in most data-focused degrees. Seek out placements in government analytical units, parliamentary offices, or think tanks such as the Resolution Foundation or Institute for Fiscal Studies where the output is influence, not just a clean dataset. This positions you in the part of the role that AI consistently underperforms.
Treat AI tools as a core technical competency
You will not out-compute AI systems, so stop competing on that axis. Instead, become expert at directing them: prompt engineering for analytical workflows, evaluating the quality of AI-generated model outputs, and knowing when to override automated conclusions. Graduates who arrive able to do this will be seen as force multipliers rather than candidates whose work AI has just made redundant.
Build sector depth alongside technical breadth
A data economist who deeply understands the energy transition, healthcare funding mechanics, or labour market inequality is far harder to replace than one who is generically skilled at analysis. Choose a sector during your degree through placements, dissertation focus, or extracurricular research, and accumulate genuine domain knowledge that requires years of contextual immersion to develop. Sector expertise compounds in a way that technical skills, increasingly commoditised by AI, no longer do.
How to get in - your routes
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