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Computational Economics Expert​/Sr. Computational Economics Expert-; Contractual; ITDDP

Job in Tacoma, Pierce County, Washington, 98417, USA
Listing for: International Monetary Fund
Contract position
Listed on 2026-09-02
Job specializations:
  • Research/Development
    Economics, Data Scientist, Mathematics, Operations Research Analyst
Salary/Wage Range or Industry Benchmark: 120000 - 160000 USD Yearly USD 120000.00 160000.00 YEAR
Job Description & How to Apply Below
Position: Computational Economics Expert/Sr. Computational Economics Expert- (Contractual) - ITDDP

Work for the IMF. Work for the World. Under the direction of the Section Chief (Data Science Section) 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 the solution of economic problems related to the Fund's business capabilities of surveillance, lending, and capacity development.

The role requires combinations of strong Computer Science and Economics skills to provide efficient and integrated solutions to the business, not only on the technological dimension, but also on the economic and quantitative modeling dimensions. Additionally, developing and delivering training on use of economic, econometric and machine learning modeling techniques and software, and advising on mathematical and high performance computational related problems are integral parts of the role.

This position requires advanced programming skills and knowledge of or experience with Economic and Econometric Modeling.

Major Duties and Responsibilities

Collaborates with economists, financial sector specialists, and other professionals from the line-of-business in the selection of the 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 the performance and extend the capabilities of economic and econometric models, including optimization for parallel computing environments and large-scale simulations, as well as improving I/O efficiency in high-throughput data workflows. Writes computational and data processing programs using high-level programming languages such as Matlab, Python, R, etc., including development and maintenance of reusable internal libraries and packages, implementation of standardized data access and reporting frameworks, and application of automated testing, documentation, and code quality assurance practices.

Supports the operation and continuous improvement of shared computational environments and software stacks, including contributions to system configuration, package management practices, and governance of analytical tools and resources, as needed to enable reproducible and efficient research workflows. 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.

Minimum Qualifications

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.

Required Competencies
  • 1. Economic and Econometric Modeling Expertise 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.
  • 2. Mathematical, Numerical, and Optimization Methods 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.
  • 3. Advanced Programming and…
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