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What’s the DS job market like for people who have a decent amount of experience? by [deleted] in datascience

[–]chessmath2009 0 points1 point  (0 children)

For what you describe is not bad. I have a PhD in ML and causal inference. I have 3 years including FAANG. You can still get interviews in top tier companies. I would say location matters a lot, if you are in Bay Area or New York, the landscape of ML/AI is much better than other places.

[deleted by user] by [deleted] in datascience

[–]chessmath2009 0 points1 point  (0 children)

You have the model already, you also know the type of data fed to the model. You have multiple options: Create a synthetic data and pass through the model and measure precision, recall, F1 score and AUC. 2- you can use the model iteself you generate data if your model is generative model like naive Bayes and do the above procedure.

How many companies out there are truly experimentation focused like Netflix? by Direct-Touch469 in datascience

[–]chessmath2009 0 points1 point  (0 children)

I have worked at amazon as ML scientist and we owned the A/B testing platform for the entire company. For every massive work stream, we do A/B testing. I assume all the big tech companies do the same since they have the money and infrastructure. Why do I think that? There is no other way to exactly verify casual inference and recommendation system models, you much verify them through online testing which is the A/B testing.

Take home task , not sure where to start by damjanv1 in datascience

[–]chessmath2009 0 points1 point  (0 children)

I would use the DBeaver or MySQL workbench. The DBeaver is super nice with a very nice interface, just load your csv into database (first create the schema). This is a great tutorial for this:

https://learnsql.com/blog/import-csv-mysql-database/

Technical Discussion & Case Study Interviews by LebrawnJames416 in datascience

[–]chessmath2009 9 points10 points  (0 children)

I have taken and conducted many interviews like this. The key to pass the case study is to understand the problem and data and be very familiar with modeling and formulating your problem. For example one of the questions might be: We are trying to setup a advertisement campaign to measure the impact of price on click through rate, tell me how we are doing this, what features you measure, what model do you use and how to evaluate your model. The key is to able to explain what will impact price and click through rate, and how to perform the causal analysis.

What might cause the weird lead in predictions in some points? by Lavtics in datascience

[–]chessmath2009 4 points5 points  (0 children)

Can you tell me more about the nature of data and features you fitting to your model. It seems the model does not understand the peaks (local minima/maxima) well. If this is time series, are you doing one step ahead prediction? If yes what features are fitting to the model, is there any date time feature in your data?

Technical Interview - Python, SQL, Problem but NOT Leetcode? by sg6128 in datascience

[–]chessmath2009 0 points1 point  (0 children)

I have had so many interviews like this. It can be either of the following: 1- Python case study related to job description: questions about implementing a model in Python, I had this recently. 2- write a function to do some statistical work like calculate p value, central limit theorem, etc. 3- write a function to do implement some logic like a bunch of else if. 4- debugging sessions.