Real, anonymous reports from people who interviewed for Data Scientist at Walmart Global Tech, newest first and distilled into what to expect across the loop.
The process I went through felt pretty smooth and professional from the start. After the recruiter conversation, the whole flow stayed focused on what they said they were looking for, and the questions stayed aligned with that. The interviewer didn’t really drift into unrelated areas, which I appreciated because it made the conversation feel clear and intentional.
After the initial recruiter touchpoint, my process quickly turned into a heavy technical sequence, and it was all virtual. I worked through a coding-focused assessment on my own time, then moved into structured interviews that went deep into ML rather than staying at a high level. The first technical round combined general discussion with a deep dive into ML concepts and also included a coding component.
The interview process typically consists of multiple rounds, often spanning several weeks to months, with a mix of technical and behavioral assessments. Candidates noted a consistent structure across rounds, which included coding, machine learning discussions, and hiring manager interviews.
Candidates faced rigorous technical evaluations, including coding assessments, SQL questions, and machine learning concepts, often with a focus on practical application and problem-solving under time constraints. The coding challenges were frequently Leetcode-style, ranging from easy to medium difficulty.
Interviews heavily emphasized machine learning, covering both theoretical concepts and practical applications, including ML design and system design discussions. Candidates were expected to demonstrate depth in their understanding of various ML algorithms and their implementations.
Behavioral interviews assessed candidates' alignment with company values and their problem-solving approaches, often intertwined with technical discussions. Interviewers aimed to evaluate how candidates would fit within the team and their communication skills.
Candidates were often asked to discuss their past projects in detail, focusing on technical implementation and decision-making processes. This evaluation aimed to connect their experience with the specific needs of the role, emphasizing business impact and KPI thinking.
The overall difficulty of the interviews was noted to be high, with many candidates feeling challenged throughout the process. Feedback on performance was often lacking, leading to frustration regarding the lack of closure after interviews.