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1. When a company asks MBB candidates about the impact or outcome they drove, it usually signals they don’t understand what MBB work entails. They’re likely not a good fit, as they won’t fully value your skill set.
2. Don’t overanalyze the feedback. Companies are often careful with what they say to avoid legal risk—most of that so-called feedback is just boilerplate. At the end of the day, it’s a vibe test. When there’s an abundance of candidates, people tend to hire those who feel familiar—whether in background, culture, or even race.
I’ve interviewed hundreds of candidates for Amazon. Amazon is looking for people who “move the ball forward” instead of just “keep their head above water”. A great candidate has had enough experiences that the candidate can discuss six times the candidate differentiated themselves from the herd AND can put numbers to quantify their own impact. However, I’ve myself been interviewed for internal transfer and had my interviewer literally lie to say that I was ‘unable to articulate my understanding of some technical point’ or I was ‘unable to provide supporting data’, when I’ve literally taught classes on the topic (at Amazon) and honed the material with actual audiences (and even feedback from L7/L8). That happens when the interviewer knows the hiring manager and the hiring manager manipulates the hiring process to get some buddy hired, but that deviates from Amazon’s practices and demonstrates why Amazon’s golden age is over.
You need to approximate or find some sort of outcome for your work. Also I think data challenges in tech usually go beyond excel (I.e writing a script to pull data, synthesizing a metric, finding work around for incomplete data).