Optimization Engineer – Commercial

Job Duties

• Formulate airline commercial decision problems into rigorous quantitative models, applying methods from operations research, data science, statistics, and econometrics to increase revenue and decrease costs
• Build and improve optimization models within Python-based decision support systems by refining assumptions, constraints, objective formulations, and solution strategies to improve performance and stability
• Design and execute structured evaluations of model features, assumptions, and parameter changes in optimization models using back-testing, controlled experiments, or simulation to assess impact on solution quality, stability, and business outcomes
• Collaborate with the Commercial Data Science team to develop and incorporate machine learning solutions into relevant decision support tools
• Extract, transform, and load data from structured/unstructured cloud and non-cloud sources via Python and SQL to create reliable inputs for decision models and recurring workflows
• Support production systems end-to-end, including troubleshooting data/model issues, diagnosing performance or stability problems, and implementing improvements to robustness, monitoring, and error handling
• Work with stakeholders to define requirements, implement enhancements, and deliver user-facing improvements to decision support tools
• Communicate analytical results, model behavior, and trade-offs through clear documentation, reporting, and presentations that facilitate understanding by both technical and non-technical stakeholders/users
• Independently identify and execute refactors that drive automation, improve performance, reduce redundancy, and increase maintainability
• Contribute to a culture of continuous improvement by documenting methodologies, applying best coding practices, self-learning in relevant mathematical/computer science topics, and staying updated on airline industry trends

Qualifications

• Education: Bachelor’s Degree in Applied Mathematics, Operations Research, Industrial Engineering, Data Science, Statistics, or related field
• Years of Experience: Minimum two (2) years of experience in a technical environment
• Proficiency in writing production-quality code in Python, utilizing libraries like Pandas/NumPy and using advanced techniques such as vectorization and parallelization
• Strong foundation in operations research techniques, including linear/nonlinear/integer programming, network/assignment models, simulation, and/or stochastic optimization
• Experience building optimization models using Python libraries such as PuLP, Pyomo, OR-Tools, SciPy, etc.
• Understanding of predictive modeling and forecasting methods such as machine learning, time series, deep learning, reinforcement learning, etc., and their implementation in Python (e.g., scikit-learn, Prophet, PyTorch, TensorFlow)
• Knowledge of statistical concepts like regression and hypothesis testing for experiment evaluation
• Ability to clearly communicate with users and stakeholders regarding feature requests, modeling choices, and experiment results through visuals, reports, and demos
• Demonstrated initiative, curiosity, and an ownership mindset in a fast-paced environment