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)
Working time Full time
Place of work Frankfurt am Main, Germany
Closing date 20.11.2026
Your team
Your role
- constructing and maintaining financial, monetary and economic databases;
- presenting data in conjunction with basic economic and statistical analyses;
- developing mathematical, statistical and econometric procedures for use in research projects;
- supporting and maintaining software tools and hardware installations.
Qualifications, experience and skills
- a bachelor’s degree or higher in economics, statistics, finance, mathematics or a related field;
- experience in at least one of the following technical areas of expertise:
- compiling large cross-sectional datasets (e.g. financial transactions data, household or firm-level survey data, or bank/firm balance sheet data);
- microeconometric modelling (e.g. fixed and random effects estimation, difference-in-difference estimation, propensity score-matching estimation, event study analysis and structural methods);
- time series modelling (e.g. vector autoregression, state-space modelling and quantile regression);
- DSGE modelling (e.g. development, solution, calibration and estimation);
- in-depth knowledge of at least one of the following high-level programming languages: Stata, MATLAB, Python or R;
- 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.
- a master’s degree (or being close to completing a master’s degree) in economics, statistics, finance, mathematics or a related field;
- familiarity with financial data providers such as Orbis, Fitch Connect, S&P Capital IQ Pro, Dealogic, BankFocus, Bloomberg or LSEG Workspace;
- familiarity with macroeconomic and financial statistics as presented in international databases (e.g. the ECB Data Portal, ECB Household Finance and Consumption Survey, statistics provided by Eurostat and/or the Bank for International Settlements, International Monetary Fund International Financial Statistics);
- experience of artificial intelligence and machine learning techniques, including their application to economic and financial data analysis;
- familiarity with solving, calibrating and simulating DSGE models using perturbation or global methods in MATLAB and Dynare.
Further information
Application and selection process