I failed an interview for being BayesianPosted by Yuling Yao on Apr 07, 2021.
I get an interview feedback from a company. I initially thought my interviews went well but it turned out that the company had a different opinion. Generally, it would be silly for me to post every job rejection. However this particular story was special because (a) the process was quite lengthy, including 9 rounds and each round is nearly hour-long, and more importantly (b) as I am now informed, the main problem they had with me was that
“You were not open-minded to solutions and first principles to the problem other than the ones that you are comfortable with.”
I recall during the interview there were a few case studies on data analysis. I proposed a complete workflow: how to design the experiment, how to make causal and decision theory adjustments, how to build models, how to make regularization, how to compute and approximate, and how to make model improvement. This is what I did in my applied data analysis all the time.
It turned out the interviewers were expecting some “first principles” such as
if(prediction problem) run a neural net; if(causal inference) run a t-test; if(model evaluation) compute AIC; if(decision theory is involved) flip a coin;
Hmm, if these magical simplifications are what I were not open-minded to, it is hard for me to regret.
Apart from reminding me of a paper review I once received “Because all MCMC methods are not scalable to big data, your new development therein is not interesting”, the feedback above might also seem to suggest that this company itself is not open-minded to candidates who are capable of solving certain problems in ways that are not familiar to the company but might have been proven successful elsewhere. To be fair, this we-are-hiring-people-who-have-certain-skills-but-nothing-more attitude makes sense in business: these companies typically have a comprehensive pipeline to problem solving and an entry level employee should really focus on implementing the given pipeline. I blame the Neanderthal inside me for not being compatible.
This whole story echoes what Andrew used to say
Making Bayes inference is the only correct thing to do when you have correct model and correct prior no matter who you are. Being Bayesian means you make Bayes inference anyway even all assumptions are wrong.
from which you could tell where I got my “not being open-minded” from, if I were enforced to accept such accusation.
In short, I am not regretful for being assertive/creative in interviews. Or put it in another way, I am not regretful for receiving doctoral level training on applied statistics (in contrast to, say, taking two online courses on “mastering all machine learning and statistics and data science and programming in 14 days”), which grants me those assertiveness and creations. As Winston Churchill pointed out:
You failed an interview for proposing your own solutions? Good. That means you’ve stood up for something, sometime in your life.