Scheduling Optimization Engineer

About You

You are a data-driven professional passionate about solving complex scheduling and resource allocation challenges. You thrive on applying advanced optimization techniques to real-world problems, balancing mathematical rigor with practical feasibility. With strong analytical and modeling skills, you design solutions that improve efficiency, reduce costs, and ensure compliance with operational and regulatory requirements.

You bring to Applaudo the following competencies:   

• 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.

You will be accountable for the following responsabilities: 

• 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.