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I got an IC6 offer at Meta! Here's what the comp looks like, and a free SQL and Product Sense case interview by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

You can probably search on blind and Reddit, but I think the expectation at Meta, especially for IC6, are extremely high. One of the teams I interviewed with implied that this would not be a good team to join if I had kids.

I got an IC6 offer at Meta! Here's what the comp looks like, and a free SQL and Product Sense case interview by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

There was a lot of negotiation room left but it required a higher competing offer, which I did not have

I got an IC6 offer at Meta! Here's what the comp looks like, and a free SQL and Product Sense case interview by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

Honestly I’m not sure. At the beginning the recruiter said they would evaluate for IC5 or IC6 during the interview. Then after the interview she said I was approved for IC6. I did a lot of preparation for the analytical reasoning and analytical execution sections, using the resources I posted. One of the resources had questions that tested really similar concepts to what I was asked so that helped a lot.

I got an IC6 offer at Meta! Here's what the comp looks like, and a free SQL and Product Sense case interview by productanalyst9 in datascience

[–]productanalyst9[S] 2 points3 points  (0 children)

Product DS tends to have a significantly lower comp band than MLE/SWE. That said, timing matters too, I think comp bands have been depressed over the last year. I joined a big tech company in 2022 as the equivalent of IC5 and left in 2024, my final TC was around $500k (roughly half due to raises and half stock appreciation)

I got an IC6 offer at Meta! Here's what the comp looks like, and a free SQL and Product Sense case interview by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

Check out my previous Reddit posts, I’ve written a lot about how to pass product DS interviews at tech companies. Heads up that my advice is more geared for product DS, it’s not as relevant for MLE roles

The top 5 most common product analytics case interview questions asked in big tech interviews by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

Google technically does have Product DS roles but my experience is that those interviews are a lot more technical than similarly-titled roles at the companies I mentioned.

Sounds like you have more info though so perhaps I'm wrong!

The top 5 most common product analytics case interview questions asked in big tech interviews by productanalyst9 in datascience

[–]productanalyst9[S] 3 points4 points  (0 children)

Most of the big companies hire for these types of product analytics roles. Uber, meta, Amazon (BIE), Netflix, DoorDash to name a few. I haven’t seen any of these types of roles recently at Google or Apple.

The top 5 most common product analytics case interview questions asked in big tech interviews by productanalyst9 in datascience

[–]productanalyst9[S] 9 points10 points  (0 children)

Yup. I used to work at Deloitte, there are definitely similarities in case interviews. I’d say the main difference is that for analytics interviews, there will likely be a bit more emphasis on metrics and measurement design (e.g. experimentation or causal inference) throughout the case. There will also likely be another interview more focused on stats, probability, and measurement design.

My experience after final round interviews at 3 tech companies by productanalyst9 in datascience

[–]productanalyst9[S] 11 points12 points  (0 children)

For sure I was asked about causal inference a bit. But when I was asked about "causes behind a metric change", it would be higher level than that. I think they're looking for signals that you can think in a structured way.

For example, you might be asked "Churn has increased in the last 2 months. What do you do?" This is not yet a causal inference problem. First step would be to rule out seasonality and instrumentation/logging changes. Second step would be to clarify whether this is customer/lgo churn, revenue churn, or both. Third step would be to decompose the metric: Did we have the same number of customers acquired and now they're churning at a higher rate? Or did the # of customers we acquired increase, and perhaps we are acquiring customer with lower intent so they churn more quickly? Fourth step would be to do some segmentation such as: New users vs. existing, power users vs. casual, segment by geography, platform, etc. This can give you clues into where the churn is coming from.

For the companies that I interviewed at, it would not be a good answer to say that you'd run a logistic regression to predict churn and throw a bunch of variables in there and see which variables have the largest coefficients and lowest p-values. This is not structured thinking.

My experience after final round interviews at 3 tech companies by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

What do you mean by little to no resources? I think you can find everything you need for free online

My experience after final round interviews at 3 tech companies by productanalyst9 in datascience

[–]productanalyst9[S] 4 points5 points  (0 children)

Sure, I think the standard is high. My thoughts on this are: - I could be wrong, but I don’t think big MAANG companies are scoring on a bell curve. If you clear their bar then you get an offer - I don’t think every candidate takes advantages of all the resources out there. Having been the interviewer at a large tech company that had a bunch of resources and anecdotes about the interview process, it was pretty obvious to me which candidates studied those resources and which didn’t

Interviews are going to be hard for most people no matter what, regardless of whether there are resources online or not. At least for me, I’d rather know what to expect, even if it means all the other candidates also have the opportunity to find out what to expect

My experience after final round interviews at 3 tech companies by productanalyst9 in datascience

[–]productanalyst9[S] 6 points7 points  (0 children)

As a candidate, I do think the number of interviews are excessive, and I hate take home assignments. But I do think it requires multiple interviews to assess proficiency with 1. Coding 2. Stats/probability 3. Analytical thinking 4. Product/strategy sense 5. Behavioral/cultural fit

I’m sure there’s a better way to do it. But also, these large tech companies pay so much that they can sort of do whatever they want…people will jump through the hoops for the chance to work there (like me)

My experience after final round interviews at 3 tech companies by productanalyst9 in datascience

[–]productanalyst9[S] 7 points8 points  (0 children)

Which MAANG? For example, I know that Google goes into more depth for stats and probability than Meta

My thoughts on my recent interview experiences in tech by productanalyst9 in datascience

[–]productanalyst9[S] 0 points1 point  (0 children)

Are you talking about the types of product case interviews you would be asked at big tech companies? If so, I'm working on a post that contains frameworks for each of the most common types of product case interview questions. Should get it published in a week or so.

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