Data Scientist

Insight Global; Boca Raton (FL); Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Insight Global in Boca Raton, FL is seeking a senior optimization specialist to formulate and deploy large-scale mathematical models (LP, MILP, MINLP) in production. You will design data pipelines, collaborate with stakeholders, and leverage solvers like Gurobi for high-performance decision systems.

Requires 3–6 years in OR/Optimization, strong Python/SQL, and cloud familiarity (AWS/Azure/GCP). The role emphasizes scalable, maintainable code and rigorous validation.

Formación

• Degree in Operations Research, Industrial Engineering, Applied Mathematics, CS, or related field or equivalent experience.
• Advanced degrees preferred for research and modeling responsibilities.
• 3–6 years’ experience in Operations Research/Optimization or related fields.

Responsabilidades

• Formulate decision problems as mathematical optimization models (LP, MILP, MINLP) from business constraints.
• Design and implement large-scale optimization models with thousands to millions of variables and constraints.
• Leverage Gurobi or equivalent solvers to build high-performance solutions and tune models.
• Develop end-to-end decision systems, integrating optimization outputs into production workflows.
• Collaborate with stakeholders to define objectives, constraints, and trade-offs for real-world decisions.
• Evaluate solution quality via scenario testing, sensitivity analysis, and stress tests.
• Scale and productionize models, including API development, automation, monitoring, and retraining.
• Build and maintain data pipelines to support optimization models with quality and performance.

Conocimientos

Python

SQL

Data pipelines

Feature engineering

Large-scale modeling

Collaboration

Educación

Bachelor's degree in Operations Research

Master's degree in Operations Research

Herramientas

Gurobi

CPLEX

OR-Tools

AWS

Azure

GCP

Descripción del empleo

• Bachelor's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field or equivalent years of experience
• Master's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field or equivalent years of experience
• 3-6 years’ experience in Operations Research / Optimization or related fields preferred
• Hands-on experience with Gurobi (strongly preferred) or similar solvers (CPLEX, OR-Tools, etc.)
• Proven experience building and deploying large-scale mathematical models (LP, MILP, etc.) in production environments
• Ability to design and maintain data pipelines for large-scale modeling workflows and experience working with large, complex datasets, including feature engineering and data validation
• Proficiency in Python and SQL, with emphasis on scalable, maintainable code
• Familiarity with cloud environments (AWS, Azure, GCP) for scalable execution

D2D:

• Formulate decision problems as mathematical optimization models (LP, MILP, MINLP), translating business constraints and objectives into structured frameworks
• Design and implement large-scale optimization models, solving complex problems involving thousands to millions of variables and constraints
• Leverage solvers such as Gurobi (preferred) or equivalent tools to build high-performance solutions, including model tuning and performance optimization
• Develop end-to-end decision systems, integrating optimization outputs into production workflows and business processes
• Collaborate with business stakeholders to define objective functions, constraints, and trade-offs, ensuring solutions align with real-world decision-making
• Evaluate solution quality through scenario testing, sensitivity analysis, and stress testing of optimization results
• Scale and productionize models, including API development, automation, monitoring, and retraining workflows
• Build and maintain data pipelines to support optimization models, ensuring data quality, availability, and performance at scale
• Communicate model structure, assumptions, and results clearly to leadership, translating complex math into actionable business insights