Computational Economics Expert/Sr. Computational Economics Expert- (Contractual)

Under the direction of the Section Chief in the IT Department at the IMF, the Computational Economics Expert provides Fund-wide services on Computational Economics using computer-based economic, econometric and machine learning modeling for economic problems related to surveillance, lending, and capacity development. The role requires strong Computer Science and Economics skills to provide integrated solutions and includes developing training and advising on mathematical and computational problems.

Responsibilities

• Collaborates with economists, financial sector specialists, and other professionals in selecting appropriate methods and data sets for economic and econometric modeling.
• Develops and implements advanced economic and econometric models.
• Undertakes research towards crafting solutions for challenges arising from economic and econometric modeling.
• Analyzes requests, designs methodology and develops programs and modules for advanced economic and econometric models.
• Researches, analyzes, and develops algorithms to improve performance and extend capabilities of economic and econometric models, including optimization for parallel computing and large-scale simulations.
• Writes computational and data processing programs using high-level programming languages such as Matlab, Python, R, including development and maintenance of reusable internal libraries and packages.
• Supports operation and continuous improvement of shared computational environments and software stacks, including system configuration and governance of analytical tools.
• Develops course materials and provides training on use of economic and econometric modeling techniques.
• Follows up current academic research on computational economics, applied mathematics and econometrics.

Requirements

• Advanced degree in Computer Science, Economics, Engineering, Applied Mathematics, or relevant field plus a minimum of four (4) years of post-graduation professional experience, or a bachelor’s degree plus a minimum of ten (10) years of post-graduation professional experience is required.
• Knowledge and experience with economic and econometric models including time series, cross-section, panel data econometrics, and macroeconomic models (DSGE, HANK, ABM).
• Proficiency in quantitative modeling, statistical estimation methods (maximum likelihood, method of moments, Bayesian inference, VAR), and use of econometric/statistical software (EViews, Stata, Matlab, Julia).
• Ability to develop problem definitions, models, and constraints from informal requirements, managing ambiguity and competing objectives.
• Expert knowledge of numerical methods, linear algebra, large-scale mathematical programming, and algorithm development.
• Strong understanding of optimization techniques including linear, nonlinear, dynamic programming, simulation-based optimization, stochastic programming, robust optimization, and approximate dynamic programming.
• Familiarity with computational complexity theory and applied/theoretical statistical learning.
• Advanced programming skills in scientific computing and data science languages such as Matlab, Python, and R.
• Extensive experience with distributed and parallel computing.
• Knowledge of machine learning algorithms, including deep learning.
• Understanding of personal computer architecture and memory organization.
• Ability to follow current academic research in computational economics, applied mathematics, and statistics.
• Strong oral and written communication skills and ability to convey higher level technical concepts to non-experts.

Skills

• Economic Modeling
• Econometric Modelling
• Time Series Econometrics
• Panel Data Econometrics
• Macroeconomic Models
• Quantitative Economic Modeling
• Statistical Estimation Methods
• Maximum Likelihood Estimation
• Method of Moments
• Bayesian Inference
• Vector Autoregression
• EViews
• STATA
• Matlab
• Julia
• Numerical Analysis
• Linear Algebra
• Mathematical Programming
• Algorithm Development
• Optimization Techniques
• Linear Programming Analysis
• Nonlinear Programming
• Dynamic Programming
• Simulation-based Optimization
• Stochastic Programming
• Robust Optimization
• Approximate Dynamic Programming
• Computational Complexity Theory
• Statistical Learning
• Scientific Computing Programming
• Python Programming
• R Programming
• Distributed Computing
• Parallel Computing
• Machine learning algorithms
• Deep Learning Models
• Computer Architecture
• Memory Organization
• Technical Communication

Languages

English