Real, anonymous reports from people who interviewed for Data Scientist at United Airlines, newest first and distilled into what to expect across the loop.
The process felt structured but fairly technical. It ran in three stages: a couple of interviews plus a coding round, all connected to the campus placements setup. After that initial flow started, I ended up facing a coding-focused evaluation rather than just discussion.
After the recruiter stage, I moved into a fairly focused technical loop. The interview content was squarely about data work and ML fundamentals, starting with SQL. I was asked SQL questions like how to find the second highest salary and how to use window functions, and the conversation also touched on a bit of pandas.
The interview process typically consists of multiple rounds, starting with an HR screening followed by technical interviews that can be spaced out over several weeks, leading to a potentially lengthy overall timeline. Candidates noted that delays between stages were common, often causing frustration.
Interviews heavily emphasize technical skills, particularly in SQL and machine learning fundamentals, including optimization techniques and core ML concepts. Candidates should be prepared for coding questions and theoretical discussions that require a solid understanding of both practical and conceptual knowledge.
Candidates are often required to present past projects and engage in case study discussions, which assess their problem-solving abilities and how they apply technical knowledge in real-world scenarios. This component is crucial for demonstrating both depth of experience and communication skills.
Behavioral interviews are integrated into the process, focusing on communication skills and cultural fit, often using the STAR method to evaluate candidates' past experiences and their relevance to the role. Candidates should be ready to articulate their thought processes and decision-making.
The overall difficulty of the interviews varies, but candidates report a consistent expectation for high technical proficiency, particularly in optimization and machine learning. Some candidates found the process challenging due to the breadth of topics covered and the depth of understanding required.
Several candidates experienced a lack of communication post-interview, leading to uncertainty about their status in the hiring process. This included delays in feedback and unclear next steps, which contributed to a negative impression of the overall experience.
