LexisNexis Legal & Professional Data Scientist Interview Questions
& Process

The interviews were conducted via Ms Teams. It was a two-stage process: a first round screening and a second round technical interview. I spoke with their HR lead recruiter. It was pretty informal, mostly just checking general knowledge about the field.

It was a typical hiring process: first interview with the hiring manager, then a data challenge, and finally a last stage interview. The process was quick, and the interviewers were friendly. However, the final interview was very poorly organized. It started with a presentation on the data challenge, which HR had asked me to prepare. Then, they expected me to present one of their products. They had sent a brochure for preparation but didn't mention I'd need to present it, leaving me unprepared. Towards the end, a project manager joined, and we went through a case study and behavioral questions. I wasn't informed about the case study or behavioral questions beforehand. The case study seemed to have a specific, predetermined answer and thought process they were looking for. It felt like they were more interested in hiring consultants or salespeople for their product rather than a data scientist. This was the first time I was asked to improvise a presentation during an interview, though I suspect it was due to disorganization rather than intentional malice. Regardless, it put me in an awkward spot. The feedback I received was that I hadn't prepared adequately, specifically for the part they hadn't instructed me to prepare.

The interview process started with a recruiter call, followed by a phone screen with two people. The final round involved a manager and others. It was a pretty straightforward process overall. Strong knowledge in NLP and ML is essential. Experience with LLM/ChatGPT is a big plus, though not strictly required. Expect questions based on Amazon principles, focusing on your experience and relevant knowledge, in a behavior-style format. You need to be able to communicate your thoughts effectively, both at a high level and by getting into specifics. Most Data Scientists here have a PhD or MS. The initial questions might seem simple, but the interviewers are looking for specific details, clear reasoning, thorough explanations, and most importantly, conciseness. Being sharp and quick is key. The interviewers are managers and tech leads, who are surprisingly sharp yet approachable. It was a good experience, though the process can be slow. Because the first questions were open-ended and appeared easy, it was hard to predict the follow-up questions. Each round of follow-ups seemed to increase in difficulty, which is how they gauged my level. As a senior-level candidate, this process was typical, expected, and quite manageable. I highly recommend preparing by studying and practicing your strongest areas.