Asana Engineering Interview Guide

The funny thing is that Asana is possibly the worst performing web app out of all the ones I use on a regular basis. I have a strong suspicion there's at least one or two O(n^2) algorithms hiding there that could use the same kind of attention and reasoning abilities they expect from their candidates.

I did an extensive set of interviews with Asana and did not enjoy the experience. After an initial screen, I was required to complete a fairly extensive take home data science problem that involved querying an AWS-hosted database, fitting a predictive model on that test data, then writing a report summarizing which factors in my model were most important and what else I might do.

Considering that (a) some of the data in the database was erroneous, but when I alerted them about it they took a long time to respond and I had to just hack a work around, and (b) it was a huge time sink in an otherwise busy week for me, I was very proud of my solution.

It was a simple random forest model, with no exploration of optimizing the parameters, and some simple entropy metrics for feature importance. I wrote nicely encapsulated SQLAlchemy for the database side, then modular sklearn and pandas code for the model fits and plots, including calibration curves in addition to accuracy. I put it all together into an 8 page TeX write up with plots.

I got virtually no feedback, then after a long time I was asked to have a technical phone interview reviewing my submission that was with two data scientists simultaneously.

They barely asked me anything about my code design. They asked a few questions about my choice of accuracy measures and entropy scores, but it was extremely hard to understand what they wanted to hear (why do companies still think that a multi-party phone call is a good use of time??).

It was a major instance of "guess the teacher's password" which was a huge turnoff for me.

Then they asked very vague, high-level questions about designing a news-feed-like interface, and how might you determine which articles are newsworthy on an individual basis. Again, super vague. I threw out all kinds of ideas about seasonality, correlation to major events, properties of your network, ... But nothing seemed interesting to them at all. They clearly wanted someone to recite some well-known stuff Facebook already used, but none of that was part of the job ad or even related at all. It was bizarre.

After that, another mysterious block of time went by with no feedback, then I got rejected with no explanation.

Later I learned that Asana has pivoted to focus on providing Agile junk for enterprise project management, so I was relieved to have dodged that bullet, but it was still a vague and time-wasting interview process, with so much magic "guess the teacher's password" nonsense.

I agree with this sentiment wholeheartedly but bounds on balanced binary trees are worth knowing (like lookup, it's log n), since that fact is one of just two reasons you'd ordinarily ever use a tree.

However, I will offer you some Interview Candidate Self Defense, which I'll repeat from something I yelled on Twitter a few weeks ago:

If interviewers ask you about bounds for data structures, pick a fight with them over hash table complexity. If they believe it's O(1), argue it's worst case O(n). If they want you to say worst case O(n), argue that it's in the real world always O(1). It doesn't matter which is right, because these kinds of questions aren't about technology, they're about status signaling. Be the alpha nerd!

As a bonus: interviewers are evaluating you for X-factors like "confidence". You know who never got dinged in an interview for lacking "confidence"? The person who yelled at the interviewer about hash table complexity.

The problem is that Asana seems to have preconceived ideas of what answers are supposed to look like, but the questions are vague and open-ended enough that even very good candidates can give good answers that simply diverge from the manner of thinking that the Asana question designer had in mind.

If the question has a well-defined answer, then this is good. If you are asking questions that the candidate "doesn't know the answer to" merely as a parochial side effect of the question's vagueness and the vast space of possible good approaches, then you're really only screening for people who happen to think about things the same way you already do, and thus are good at guessing what you want to hear.

I'd argue that this is very bad in terms of diversity of thought.