After a recruiter step, I went through an extensive sequence focused on data work and model building. The first technical conversations covered data manipulation and EDA, then moved into modeling. The goal was to see whether I could connect statistical thinking and practical data familiarity to the way I constructed and interpreted models. The process felt thorough rather than trick-based. The questions varied with my background, but the common thread was whether I could take messy data through cleaning and analysis to a finished, defensible result.
I had multiple Python-focused technical rounds. In one, I worked end to end from data through a model and analysis. It wasn’t a small toy exercise. I had to get to the point where the model’s story made sense. The other rounds were also technical and coding focused, with practical data analysis and modeling tasks and an expectation that I would produce a complete solution within the interview format.
There were also soft-skill interviews that included project and team management topics. The tone stayed positive throughout, and I felt like I was doing well. After I finished everything, though, I waited a long time and was rejected without much clarity. The process felt structured and people-oriented, which made the final decision frustrating, especially after such a thorough process.
Location: New York, NY. Overall feedback: Positive. Offer status: No offer.
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