Senior Data Scientist, Optimization Track

About the Role

This is a senior data scientist position focused on mathematical optimization and operations research. It requires expanding on existing data science and machine learning skills to include exact/approximate methods for linear and nonlinear optimization problems.

Key Responsibilities

• Collaborate with cross-functional teams including business stakeholders, domain experts, and engineers to identify and frame challenging problems in mathematical optimization and operations research.
• Design, develop, and deploy optimization solutions that directly impact aircraft assignment, crew scheduling, and other operational areas. This includes building predictive models for forecasting demand, disruptions, or operational KPIs.
• Validate models and solutions through rigorous testing, scenario analysis, and sensitivity studies, clearly communicating results and trade-offs to both technical and non-technical audiences.
• Work closely with application development teams to operationalize and integrate optimization capabilities into their software systems, advising on data requirements, API design, performance, and monitoring.
• Monitor and maintain production optimization services, troubleshoot issues, and continuously iterate on models and algorithms as business needs evolve.
• Stay current with advances in operations research, optimization solvers (e.g., Gurobi), algorithmic improvements, and relevant data science techniques; evaluate and recommend new tools or libraries for adoption.

Requirements

• BS in Computer Science, Mathematics, Physics, or a related discipline is required. PhD and MS degrees related to mathematical optimization are highly desired.
• Advanced programming skills in Python, conversant with data structures, algorithm design/analysis, and object-oriented programming paradigms.
• At least 2 years of relevant technical experience in two or more of the following areas:
• Intermediate or advanced skills in operations research, such as linear/integer/mixed-integer programming. Familiarity with simulation, queuing theory, network, and decision analyses is a plus.
• Good understanding of subareas of optimization algorithms, such as gradient-based methods or metaheuristic methods.
• Familiarity with common Python optimization libraries (NumPy, SciPy, CVXPY, PuLP, Pyomo) and modern optimization platforms (Gurobi, CPLEX, Google OR-Tools).
• Intermediate-level hands-on skills in predictive modeling, shallow machine learning, and information retrieval.
• Prior experience or knowledge in aviation/airlines operation is a plus.
• Some hands-on experience with AWS, GCP, or similar public cloud environments.
• Excellent interpersonal and communication skills for working with both technical staff and non-technical business users.
• Experience with Agile/Scrum/Kanban methodologies is a plus.

Key skills/competency

• Mathematical Optimization
• Operations Research
• Python Programming
• Predictive Modeling
• Machine Learning
• Gurobi
• CPLEX
• Algorithm Design
• Cloud Computing (AWS/GCP)
• Stakeholder Collaboration