Data Scientist

Insight Global; Boca Raton (FL); On-site; USD 120,000 - 150,000; Full time

Job summary

Insight Global is seeking a highly skilled Optimization Engineer in Boca Raton, FL to develop and deploy large-scale mathematical models (LP/MILP) in production. You will design data pipelines, collaborate with stakeholders to define objectives, and tune models using Gurobi and other solvers.

Strong Python/SQL and cloud experience are required. Join a team that translates complex mathematical outputs into actionable business decisions and scales models through API development, monitoring, and

Qualifications

• 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

Responsibilities

• Formulate decision problems as mathematical optimization models (LP, MILP), 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

Skills

Optimization modeling

Python programming

SQL

Data pipeline design

Stakeholder communication

Education

Bachelor's degree in OR/Industrial Engineering/Applied Math/CS

Master's degree in OR/Industrial Engineering/Applied Math/CS

Tools

Gurobi

CPLEX

OR-Tools

AWS

Azure

GCP

API development

Job description

• 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