General Information
Type of contract Traineeship
Who can apply? EU nationals eligible for our traineeship programme
Grant The trainee grant is €1,170 per month plus an accommodation allowance (see further information section), The trainee grant is €2,120 per month plus an accommodation allowance (see further information section)
Working time Full time
Place of work Frankfurt am Main, Germany
Closing date 20.10.2026
Your team
Your role
- work closely with the Division’s economists to develop and maintain structural, semi-structural, time series, machine learning and deep learning models for the euro area and its largest member countries;
- programme and maintain the mathematical, statistical and econometric procedures required to support policy input and research projects and enhance analytical, numerical and econometric tools;
- contribute to the ECB and Eurosystem staff macroeconomic projections for the euro area, e.g. by supporting the preparation and presentation of model-based forecast interpretations and risk scenarios;
- work with, maintain and update datasets (including monetary, financial, economic, textual and other unstructured data) to support research projects on forecasting, macroeconomic modelling and policy analysis;
- contribute to quantitative analyses and analytical projects that inform policy reports and working papers, including those intended for external publication.
Qualifications, experience and skills
- for a traineeship paid at €2,120: a master’s degree and at least two years of PhD studies in economics, statistics, finance, data science, applied mathematics, computer science or a related field;
- for a traineeship paid at €1,170: a bachelor’s degree or higher in economics, statistics, finance, data science, applied mathematics, computer science or a related field;
- in addition to the above, practical experience of data-intensive projects and familiarity with macroeconomic and financial statistics from international databases, such as the ECB Data Portal, IMF International Financial Statistics and those maintained by Eurostat, the OECD or Bloomberg;
- a sound knowledge of spreadsheet and data visualisation tools, such as MS Excel, MS PowerPoint, R and Python graphics libraries;
- strong programming skills with practical experience, preferably in MATLAB, Python or R;
- experience in macroeconomic modelling, particularly in one or more of the following areas: semi-structural, DSGE, Bayesian vector autoregression (VAR) and factor models; sequence-space Jacobians, structural identification and non-linear filtering in time series models; textual analysis and natural language processing, deep learning, random forests and other machine learning methods; or econometric modelling software (e.g. MATLAB toolboxes or Dynare) and deep learning applications (e.g. TensorFlow, PyTorch or Hugging Face);
- a good knowledge of the MS Office package;
- an advanced (C1) command of English and an intermediate (B1) command of at least one other official language of the EU, according to the Common European Framework of Reference for Languages.
- for a traineeship paid at €1,170: at least one year of completed master’s studies in economics, statistics, finance, data science, applied mathematics, computer science or a related field;
- a sound understanding of macroeconomics and monetary economics;
- knowledge of version control systems (e.g. Git);
- a completed project (e.g. a thesis, dissertation or research paper) demonstrating advanced quantitative modelling and/or data processing skills; this may include the use of DSGE models, Bayesian techniques, advanced machine or deep learning methods and non-standard or complex data sources (e.g. web data or newspaper articles);
- familiarity with one or more of the following: designing database schemas, writing SQL queries, connecting to databases programmatically (e.g. with Python, R or MATLAB), using object-relational mapping (ORM) tools such as SQLAlchemy and working with cloud or NoSQL databases;
- familiarity with software development and machine learning engineering practices, such as automated testing, containerisation or cloud computing, for building reproducible and scalable analytical workflows.
Further information
Application and selection process