At United Airlines, data is the fuel that powers one of the world’s largest aviation networks. A Data Scientist at United Airlines does not work in a vacuum; you are tasked with solving some of the most complex logistical, operational, and commercial challenges in the travel industry. From optimizing flight schedules and crew assignments to predicting maintenance needs and dynamic ticket pricing, your models directly impact millions of passengers and shape the strategic direction of the airline.

The role sits at the intersection of advanced machine learning, operations research, and business strategy. Whether you are embedded in Revenue Management, Network Planning, Operations, or Customer Experience, you will work with massive, real-time datasets that capture the volatility of global travel. The sheer scale of operations means that even a fractional percentage improvement in fuel efficiency, gate utilization, or pricing models translates to millions of dollars in business value and a significantly smoother travel experience for customers.

What makes this position uniquely compelling is the tangible nature of your impact. You will build and deploy models where the outputs are visible in physical operations—affecting which planes fly where, how crews are positioned globally, and how the airline navigates disruptions like severe weather. For a curious and driven Data Scientist, United Airlines offers an intellectually rigorous playground of mathematical modeling, optimization, and predictive analytics.

To help you prepare effectively, we have analyzed real interview experiences to identify the core patterns in the types of questions you will encounter. These questions represent a mix of theoretical rigor, practical coding, and situational problem-solving.

Operations Research & Optimization

Because of the heavy logistical demands of running an airline, United Airlines frequently tests candidates on traditional mathematical optimization. Expect questions that assess your ability to model constraints and objectives mathematically.

• Formulate a Mixed-Integer Linear Programming (MILP) problem to solve a basic resource allocation challenge.
• What is the difference between convex and concave optimization, and why does this distinction matter when selecting a solver?

Succeeding in the United Airlines hiring process requires a balanced preparation strategy. You cannot rely solely on machine learning knowledge; you must also demonstrate strong analytical foundations and business acumen.

Mathematical & Optimization Fundamentals – Many teams at United Airlines rely heavily on Operations Research. You should review linear programming, constraint satisfaction, and optimization algorithms. Be ready to write out mathematical formulations on a whiteboard or digital canvas.

Pragmatic Problem Solving – Interviewers want to see how you think. When presented with an open-ended case study, focus on structuring your thoughts logically. State your assumptions clearly, define your metrics of success, and outline a step-by-step methodology before diving into technical details.

Technical Communication – You must be able to translate complex data science concepts into actionable business insights. During project walkthroughs, emphasize the business impact of your work, such as costs saved, revenue generated, or operational efficiency gained.

Behavioral Alignment – Use the STAR method (Situation, Task, Action, Result) to structure your behavioral answers. United Airlines values collaboration, safety, efficiency, and customer-centricity. Ensure your stories highlight how you navigated team dynamics and delivered measurable results.

The interview process for a Data Scientist at United Airlines is rigorous and thorough, typically taking anywhere from four to eight weeks, depending on the role level and team. The company places a strong emphasis on both theoretical knowledge and practical application, ensuring that candidates can handle the complex operational data of a major carrier.

The journey begins with a standard recruiter screen to align on your background, salary expectations, and overall fit. Following this, you will transition into technical evaluations, which often start with an online assessment (OA) consisting of coding challenges, mathematics/probability exams, or machine learning multiple-choice questions. Depending on the team, you may also be handed a take-home case study with a 48-hour window to formulate a structured approach to an operational problem.

The final stages involve deep-dive technical interviews and behavioral panels. For some US-based roles, United Airlines invites final-round candidates to their corporate headquarters in Chicago, Illinois. This onsite experience allows you to meet the team in person, present your case study or past projects, and participate in behavioral discussions.

This visual timeline represents the standard path a candidate takes from application to offer. It highlights the transition from initial screening and independent technical assessments to highly collaborative, interactive panels. Use this structure to pace your preparation, focusing first on core technical concepts before refining your presentation and behavioral storytelling skills.

To stand out during the evaluation process, you must understand the specific competencies United Airlines interviewers are trained to assess.

Operations Research & Optimization

This area is critical for teams focused on network planning, scheduling, and revenue management. Interviewers want to see if you can translate complex physical constraints into mathematical formulations.

Be ready to go over:

• Linear Programming (LP) Formulations – Setting up objective functions and linear constraints to maximize efficiency or minimize cost.
• Mixed-Integer Linear Programming (MILP) – Handling decision variables that must be integers (e.g., assigning a specific aircraft to a route).
• Heuristics and Metaheuristics – Utilizing genetic algorithms, simulated annealing, or custom heuristics when exact optimization takes too long.
• Advanced concepts (less common) – Quadratic programming, non-linear optimization, and analyzing NP-hard complexity classes.

Example questions or scenarios:

• "Formulate an optimization model to schedule crew shifts while adhering to federal rest regulations and minimizing total overnight hotel costs."
• "Explain how a genetic algorithm determines when to stop iterating and how you prevent it from getting stuck in local optima."

Core Machine Learning & Statistics

For predictive modeling roles, you must demonstrate a deep statistical foundation. The team values candidates who understand the mathematical trade-offs of their modeling choices.

Be ready to go over:

• Ensemble Techniques – Deep understanding of bagging, boosting, and stacking, including how hyperparameters affect model variance.
• Probability Distributions & Estimation – Working with binomial, Poisson, and normal distributions, as well as calculating maximum likelihood.
• Evaluation Metrics – Choosing the correct metrics (e.g., Precision-Recall AUC vs. ROC AUC) based on business costs and class imbalance.

Example questions or scenarios:

• "We want to predict whether a flight will be delayed by more than 30 minutes. How would you handle the extreme class imbalance in our historical data?"
• "Walk me through the mathematical derivation of a hypothesis test comparing the performance of two different pricing algorithms."

SQL & Practical Coding

You must prove that you can manipulate large datasets efficiently without relying purely on automated GUI tools.

Be ready to go over:

• SQL Window Functions – Using ROW_NUMBER(), RANK(), LEAD(), and LAG() to analyze sequential flight data.
• Data Preprocessing in Python – Writing efficient, vectorized Pandas code to clean, filter, and aggregate time-series data.
• Algorithmic Problem Solving – Basic data structures, string manipulation, and complexity analysis (Big O notation).

Example questions or scenarios:

• "Write a SQL query to calculate the rolling 7-day average of passenger bookings for each departure airport."
• "Given a list of flight legs, write a Python function to find if there are any overlapping schedules for a single aircraft."

Case Studies & Project Presentations

This is where you demonstrate your ability to act as a consultant to the business. You will be evaluated on your structured thinking, creativity, and communication.

Be ready to go over:

• The Take-Home Case Study – Presenting your approach to an open-ended problem, justifying your model choice, feature engineering, and deployment strategy.
• Resume Project Deep-Dives – Explaining a past project in detail, focusing on your specific contributions, the technical challenges faced, and the ultimate business impact.

Example questions or scenarios:

• "Walk us through the 48-hour case study you prepared. Why did you choose this specific modeling approach over the other options?"
• "In your past project, how did you validate that your model's predictions were actually driving the business metric you claimed?"