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GAMS (General Algebraic Modeling System) is a high-level modeling language and integrated development environment for constructing and solving mathematical optimization models. Developed in the 1980s by Alexander Meeraus and colleagues at the World Bank, GAMS is purpose-built for operations research, industrial engineering, and applied economics. Unlike general-purpose languages where you write optimization algorithms, GAMS lets you specify the mathematical structure of your problem: variables, constraints, objective function. GAMS then automatically selects appropriate solvers (linear, nonlinear, mixed-integer) and passes your model to them.
GAMS' power lies in how cleanly you can express complex optimization problems. A supply chain optimization with thousands of products, factories, and distribution centers would require hundreds of lines of complex code in Python or C. In GAMS, the same problem is expressed declaratively: define the sets (products, factories, routes), parameters (costs, capacities), variables (flows, production levels), and constraints (conservation, capacity). GAMS compiles this into solver input automatically.
Hire a GAMS developer when you're solving optimization problems where the mathematical structure is complex and the business value of correctness is high. The canonical use case is supply chain planning: optimizing production schedules, inventory levels, and distribution routes for hundreds of products and locations. Another strong case is energy systems: modeling power generation dispatch, renewable integration, microgrid control, or capacity planning where the mathematical structure is inherently complex.
You should also hire GAMS for financial optimization: building portfolio optimization models, optimizing hedging strategies, or solving funding optimization problems. GAMS is not the choice for general-purpose software development, web applications, or simple analytics.
Junior (1-2 years): Understands GAMS syntax and basic modeling, can build simple linear optimization models, understands the difference between set definitions and variable declarations, has worked with one or two domains.
Mid-level (3-5 years): Proficient in building moderately complex models (mixed-integer, nonlinear), understands how to structure models for scalability and solver efficiency, can diagnose infeasibility and numerics issues.
Senior (5+ years): Architected large-scale optimization systems, deep knowledge of mathematical modeling, understanding of advanced solver techniques (column generation, cutting planes, interior point methods), can mentor others.
Tell me about the largest optimization model you've built or migrated. How many variables and constraints, and what did it optimize? Look for specific numbers and problem description. Someone who built a model with 100k variables and solved it in an hour understands scalability.
Describe a time when you found your optimization model was infeasible in production. How did you debug it? This reveals practical problem-solving. Strong answers mention checking constraint logic, verifying data, using solver diagnostics, and communicating findings to domain experts.
Explain the difference between linear and nonlinear optimization models and when you'd choose each. This tests fundamentals. A strong answer discusses solver requirements, solution time, and problem structure.
Write a simple GAMS model for a facility location problem with demand constraints. Expect something showing set definitions, parameters (distances, costs), variables (facility open/close, assignment flows), objective function, and constraints.
Write a GAMS model for a simple production planning problem: minimize production and inventory costs over multiple periods with demand constraints, production capacity, and inventory bounds. This is realistic for operations planning. Evaluation rubric: (1) Correct set definitions (2) Proper variable declarations (3) Correct objective function (4) Correct constraints (5) Clean, readable code (6) Bonus: includes solver selection and options.
Junior (1-2 years): $32,000-$48,000/year
Mid-level (3-5 years): $55,000-$85,000/year
Senior (5+ years): $95,000-$145,000/year
Staff/Architect (8+ years): $155,000-$230,000/year
US market rates for operations research specialists are 90-160% higher than LatAm rates. The LatAm advantage is substantial because senior optimization engineers cost significantly less in South America.
Latin America has a strong operations research and industrial engineering tradition rooted in university programs and growing optimization-heavy industries. Universities in Brazil (USP, UFRJ), Argentina (UBA, ITBA), and Mexico (UNAM, Monterrey Tech) have excellent OR programs. The energy sector in Brazil, supply chain operations in Mexico and Argentina, and financial services across the region create demand for optimization expertise.
Timezone overlap is excellent: most LatAm GAMS developers are UTC-3 to UTC-5, providing 6-8 hours of overlap with US East Coast and 3-5 hours with US West Coast. Optimization work that benefits from synchronous collaboration on model design is well-served.
Share your optimization problem: What are you optimizing? (Supply chain, energy, finance, something else?) What's the problem scale? Any solver preferences? South's network includes operations research professionals and mathematical modelers across Latin America with GAMS expertise.
If a hire doesn't work out, we match you with a replacement within 30 days at no additional cost. Start your search at South.
GAMS is for large-scale optimization and mathematical modeling: supply chain planning, energy systems, portfolio optimization, production scheduling, facility location, and any complex problem involving optimization.
Yes. GAMS is the industry standard for supply chain optimization: production planning, inventory management, distribution network design, and demand forecasting. Many Fortune 500 retailers, manufacturers, and logistics companies use GAMS for core supply chain decisions.
Python for prototyping and general optimization work. GAMS for complex models in production and for transparency with non-technical stakeholders. GAMS models are often more maintainable and portable across solvers.
Senior GAMS developers in LatAm range from $95,000-$145,000/year. Mid-level developers are $55,000-$85,000/year. US rates are typically 90-160% higher for equivalent seniority.
Most matches happen within 7-14 business days. GAMS is specialized, so the candidate pool is focused.
Yes. South handles employment, payroll, benefits, equipment, and local tax compliance.
Python — Often used for data preprocessing, postprocessing, and scripting around GAMS models.
Operations Research — Domain expertise that pairs naturally with GAMS for optimization problem formulation.
