Optimization Expert

Há 3 dias

Belo Horizonte, Minas Gerais, Brasil beBeeScheduling Tempo inteiro R$80.000 - R$100.000

We are seeking a seasoned scheduling optimization expert to drive the development and implementation of advanced optimization models.

About You

You are a results-driven data analyst with expertise in solving complex scheduling and resource allocation challenges. Your strong analytical and modeling skills enable you to design solutions that improve efficiency, reduce costs, and ensure compliance with operational and regulatory requirements.

  • Advanced knowledge of optimization modeling (integer programming, constraint programming, heuristics/metaheuristics).
  • Proven experience developing and maintaining large-scale scheduling models in applied environments.
  • Ability to incorporate operational rules, contractual agreements, regulatory requirements, and business constraints into model formulations.
  • Hands-on experience with optimization solvers (e.g., Gurobi, CPLEX, OR-Tools).
  • Proficient in programming languages such as Python, Julia, or C++.
  • Strong understanding of data analysis for planning based on event calendars, service times, passenger volumes, and destination planning.
  • Analytical mindset with strong problem-solving skills and attention to detail.
  • Excellent communication skills to work effectively with technical and non-technical stakeholders.
Responsibilities

Key responsibilities include:

  • Developing and maintaining advanced optimization models to solve large-scale scheduling problems.
  • Building models that consider event calendars, service times, passenger volumes, and destinations using available data.
  • Integrating operational rules, contractual obligations, regulatory requirements, and business constraints into model formulations.
  • Applying optimization solvers (Gurobi, CPLEX, OR-Tools) to generate feasible, efficient schedules.
  • Collaborating with operations, planning, and technology teams to align outputs with real-world needs.
  • Analyzing results to identify cost savings, efficiency improvements, and compliance risks.
  • Continuously refining models to adapt to changes in resource availability, demand patterns, and operational constraints.

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