2 months ago
Las Vegas, NV, USAMid Level
Base Salary
$80k - $100k/yr
Responsibilities
- Formulate airline commercial decision problems into quantitative optimization models using operations research, data science, statistics, and econometrics.
- Build and improve Python-based optimization and decision-support systems by refining assumptions, constraints, objectives, and solution strategies.
- Evaluate model features, assumptions, and parameter changes through back-testing, controlled experiments, and simulation.
- Collaborate with Commercial Data Science to incorporate machine learning solutions into decision-support tools.
- Extract, transform, and load data from cloud and non-cloud sources using Python and SQL for decision models and recurring workflows.
- Support production systems by troubleshooting data and model issues, diagnosing performance and stability problems, and improving monitoring, error handling, and robustness.
- Partner with stakeholders to define requirements, implement enhancements, and deliver user-facing improvements.
- Communicate model behavior, analytical results, trade-offs, and methodologies through documentation, reports, presentations, and demos.
- Independently execute refactoring and automation work to improve performance, reduce redundancy, and increase maintainability.
Requirements
- Bachelor’s degree in Applied Mathematics, Operations Research, Industrial Engineering, Data Science, Statistics, or a related field.
- At least two years of experience in a technical environment.
- Production-quality Python programming experience, including Pandas/NumPy, vectorization, and parallelization.
- Strong knowledge of operations research techniques including linear, nonlinear, and integer programming; network and assignment models; simulation; and/or stochastic optimization.
- Experience building optimization models with libraries such as PuLP, Pyomo, OR-Tools, and SciPy.
- Understanding of predictive modeling and forecasting methods including machine learning, time series, deep learning, and reinforcement learning, with Python implementation experience.
- Knowledge of regression, hypothesis testing, SQL querying, and exploratory data analysis.
- Ability to communicate modeling choices, feature requests, and experiment results to technical and non-technical stakeholders.
- Preferred: master’s degree or higher, airline economics experience, commercial solver experience with tools such as Gurobi, CPLEX, or FICO Xpress, AWS and GitHub familiarity, JavaScript and Excel VBA exposure, and experience with GitHub Copilot and Claude Code.
- Must be authorized to work in the United States and pass a criminal background check.
Tech Stack
Categories
BackendData Science
