Anunciada dia 5 outubro
Descrição
Job Overview
As a seasoned Scheduling Optimization Specialist, you will be responsible for designing and implementing cutting-edge optimization models to tackle complex scheduling challenges.
With a passion for data-driven problem-solving, you will thrive in an environment where mathematical rigor meets practical feasibility.
By leveraging advanced optimization techniques and incorporating operational rules, contractual agreements, and business constraints, you will create innovative solutions that drive efficiency and reduce costs.
* 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).
* Proficiency 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.
* Develop and maintain advanced optimization models to solve large-scale scheduling problems.
* Build models that consider event calendars, service times, passenger volumes, and destinations using available data.
* Integrate operational rules, contractual obligations, regulatory requirements, and business constraints into model formulations.
* Apply optimization solvers (Gurobi, CPLEX, OR-Tools) to generate feasible, efficient schedules.
* Collaborate with operations, planning, and technology teams to align outputs with real-world needs.
* Analyze results to identify cost savings, efficiency improvements, and compliance risks.
* Continuously refine models to adapt to changes in resource availability, demand patterns, and operational constraints.
