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Data Scientist

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Onsite

Data Scientist

Insight Global

Data Scientist

Skills

Google Cloud PlatformSQL QueriesPythonAWSAzure

1 day ago

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Important information


Contract type:

Permanent

Salary:

$130,000 – $140,000 / year

Location:

Boca Raton, FL, USA

Work mode:

Onsite

Published on:

22 October 2026

What they need


Position: Data Scientist

Location: Boca Raton, FL (On-site)

MUST HAVE:

  • 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

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