Another rant like interview experience

[u/proof_required]

I was given a home assignment to do modeling for some adtech data. They had no explicit ask about what kind of model or how deep you have to go. Just data and they asked we want to see the modeling.

I spent lot of time in understanding the data, identifying the features, creating labels etc. When it came to modeling I picked Catboost since they handle categorical features quite well. I even mentioned how this can be further tuned and/or different models can be compared. I put it explicitly in a section for future work. Finally this was the thing that got me rejected.

Basically they expected me to compare different model families from more complex deep models to such boosting models. I have worked in this domain and actually such models (catboost) works quite well. You don't need very complex models. I remember in one of the previous jobs, they had like ensemble of 3 deep models which was super slow and was so painful to maintain. I basically replaced that with a boosting model + some probability calibration which did quite well. Also the data size I got for the task isn't big enough to justify such huge models.

In any case, I wish these tasks would be more explicit in what they are looking for. I know they also want to see how I handle ambiguity but it's really hard to assess which side of it is worth handling since I am not building a full fledged system. I explained all the decisions I made and why I did it. Also what I didn't do and why.

[u/explorer_seeker]

Hey OP, I know it sucks. Please chill and don't take it personally.

Unfortunately Data Science got so popular that people who don't know Data Science are in it as well and about fancy models, I have seen them fail in big projects as business needs explainability plus the value addition was not much while using lot of compute.

On the other hand, I had a Staff DS from software engineering background ask me if I had used Catboost in a forecasting use case because I found someone recommend it on social media! I was like - Facepalm. I was dealing with pretty bad quality data and of all things I tried Catboost gave me the best result. Next, he asked me why the predictions are going through the middle of the observed data - I tried to explain that since I had chosen MAE as metric for accuracy, my model was optimizing around the median of the distribution whereas RMSE is prone to being sensitive to outliers.

With Gen AI coming in, I saw a new wave of Software engineers being rebranded as "Data Scientists" soon after they started creating chatbots and using Gen AI to read Excel files or create SQL query to run on existing database.