Phone screen with the hiring manager, technical phone screen with the principal data scientist, take-home assignment, getting feedback for the take-home over the phone, 5-hour onsite (30-minutes presentation plus 5 one-on-one sessions). The positive side of the experience - the hiring team and the recruiters all have acted reasonably professionally and efficiently. I appreciate that the recruiter actually took the time to call me to tell me about the rejection instead of just sending over an email with canned script. However, I sensed several red flags throughout the process. First of all, more than one person on the hiring team misrepresented how the take-home assignment is positioned in the entire interview process. They both made it sound as if the take-home assignment were only given to candidates who have borderline performance on the technical screen and it'd be waived if someone had nailed the tech screen. However, based on how the onsite interview is designed and the extent to which the one-on-one conversation revolves around the case study, it became very clear that the take-home assignment is mandatory for all the candidates. The second red flag - from my own research and conversation with LH’s employees, female is extremely underrepresented on the Data/Analytics team. There is NOT a single female data scientist besides two female data analysts. Furthermore no one proactively addressed what caused such an imbalance on the team. For a Fintech firm, the majority of data scientists appear to be male with an engineering degree with minimal or no background in economics or finance. For my part, I delivered a very solid case study and presentation that demonstrated my background in economics and consulting as well as my ability to turn data into easy-to-understand visualizations, and I went above and beyond to acquire third party data (including using APIs) to enrich the single set of data the firm provided. I got very positive feedback after my presentation, and I completed the technical whiteboard successfully as well. The hiring manager acknowledged more than once (in emails and verbally) that my domain knowledge of the real estate industry and my unique background of consulting and data science are extremely valuable to their business. However, the ultimate rejection was based on the feedback that I didn’t give adequate answers to some skills/techniques mentioned in my resume. Now that I think of it, there were only three questions directly related to my resume. One of the questions asked me to describe a past project in standardizing and integrating datasets. The other two came from a senior person from a cross-functional team. He asked me to explain WHAT machine learning and gradient boosting are. I explained what machine learning is in a light-hearted manner - I cited a comic to explain that what’s underneath the hype of machine learning is fundamentally statistical modeling. And exactly because I have extensive experience in data modeling, I shared my thoughts that despite all the hype, there are limitations with machine learning in explaining causal relationships. For anyone with some basic understanding of data science/machine learning, I believe this would appear as an intuitive and thoughtful answer, and the answer was meant to be catalyst for further discussions instead of a definitive answer. Also, the interviewer asked me these questions AFTER he appeared to be very impressed with some of the questions I asked specifically about risk management.