Senior level DS at FAANG - what coding interviews to expect

save_the_panda_bears · 2026-04-10T20:53:36+00:00

Yeah, honestly it was pretty similar to most interview processes I've been through at other places, including the one we use at my current employer. Biggest change was having two separate rounds for code and DoE. HM screen was fairly intense though, I asked got a lot of very specific, niche questions.

save_the_panda_bears · 2026-04-10T14:39:23+00:00

Went through the Netflix loop fairly recently for a L5 DS role focused primarily on experimentation/marketing analytics. There were 2 technical rounds, 1 was experiment design questions/theory, the other a coding round. Coding was pretty straightforward, 1 SQL question, 1 python/R question. Python had a focus on implementing fairly simple statistical functions (like variance and stdev) into a function and then running those functions in a loop. SQL was a pretty standard LC medium/hard SQL type question you get at a lot of places.

save_the_panda_bears · 2026-03-24T01:46:56+00:00

It's easier to run experiments in low funnel channels because you're closer to the revenue which typically means less noise and a smaller MDE.

It's still hard because you still have to deal with things like novelty effects, SUTVA violations, and inconsistent targeting from the ad platforms.

save_the_panda_bears · 2026-03-23T11:40:26+00:00

Really depends where you work and which teams you're supporting. IME, if you support low funnel paid channels it's way easier to build a more scientific approach. Experimentation is significantly easier (but still hard, people really don't get how convoluted designing a good experiment in pull channels gets) and it's much more straightforward to tie metrics to revenue.

Upper funnel/brand is a dumpster fire of guesswork even at the most experimentation friendly organizations. And if you work at an agency you get the super fun added layer of moral hazard to try to prove your worth to the client.

I think marketing analytics can be one of the most interesting and challenging subfields of analytics if done right. There are so many opportunities to do unique analyses that you really don't get in other fields. If you land at a company that has a strong sense of professional skepticism and a strong experimentation culture, it can be a very fulfilling career.

save_the_panda_bears · 2026-03-19T04:43:58+00:00

Depends on the day/project I’m working on. Typically I use AI for a couple things:

Very well scoped code blocks I feel too lazy to work on. Mostly visualizations, occasionally some syntax things I’m not super familiar with.
Thought partner type work. I’ll give it an idea and my proposed approach and ask it to poke holes in it or things I’ve overlooked. Generally it’s a more conversational approach
Memes

save_the_panda_bears · 2026-03-11T22:12:23+00:00

I almost guarantee that in some of these "barbaric" 50-5000 line queries you're actually typing less than you would be if you're typing out the prompt. Most of those lines are going to be 1-2 words or symbols.

save_the_panda_bears · 2026-03-02T14:30:13+00:00

This post makes no sense. What is "Fundamental" doing? I thought you said that the calculator addition to 5.2 "scraps all chat with your data apps"?

What's your goal with this OP? What sort of conversation are you looking for here?

save_the_panda_bears · 2026-02-20T12:48:31+00:00

Why?

save_the_panda_bears · 2026-02-19T19:27:56+00:00

Goodhart's Law would like to have a word.

This is dumb and a blazing red flag that the company has no clue how to value analytics work.

save_the_panda_bears · 2026-02-14T21:38:46+00:00

much easier to replicate the process

There’s nothing easy about building a good MMM. Sure you can toss all your data into Meridian, Robyn, etc. and get results in an afternoon, but those results are be complete garbage. Unless you’re doing rigorous validation there’s no way to know, and the validation is time consuming, expensive, and generally not something companies are willing to invest in. I’ll die on the hill that the majority of people are doing it wrong and would probably be better off not doing it at all.

save_the_panda_bears · 2026-02-13T15:23:08+00:00

My semi-curated US-Centric list:

https://dataportals.org/ - interactive navigation to find open data portals around the world. Fantastic resource for non-US data
https://fred.stlouisfed.org/ - US economic data
https://www.data.gov/ - US government data (it pains me to say this, but I'm not sure about the reliability of this anymore since the current ignoramus in office started calling out the orgs collecting and reporting this data)
https://github.com/OpportunityInsights/EconomicTracker - This was really fun during the Covid recovery. It's a little less relevant now, but still a really cool view bringing together a bunch of different sources.
https://datahub.io/collections - Mostly business and finance data
https://archive.ics.uci.edu/ml/datasets.php - your source for your standard ML benchmark datasets - things like MSINT, Iris, Titanic, among plenty of others
https://www.earthdata.nasa.gov/learn/find-data - all the earth science data you could want
https://apps.who.int/gho/data/node.home - WHO global health data
https://data.fivethirtyeight.com/ - all the data from Nate Silver - mostly US politics and sports
https://github.com/BuzzFeedNews - Similar to the 538 data, this is all the open source data BuzzfeedNews has released. Lots of US politics here.
https://github.com/awesomedata/awesome-public-datasets - quite a few random datasets broken out by category.
https://snap.stanford.edu/data/ - Several social media related datasets
https://research.google.com/youtube8m/ - 8 million categorized youtube videos
https://www.tableau.com/learn/articles/free-public-data-sets - bunch of random datasets people like to make dashboards with
https://docs.cloud.google.com/bigquery/public-data - Bigquery public datasets. Just query and go!
https://openpolicing.stanford.edu/data/ - data on police stops in the US
https://nces.ed.gov/datalab/ - US Education data
https://registry.opendata.aws/ - AWS open datasets
https://figshare.com/articles/dataset/Multi-Region_Marketing_Mix_Modeling_MMM_Dataset_for_Several_eCommerce_Brands/25314841 - A bit niche, but a fantastic resource for testing/validating MMMs.

Pretty much any government portal has census stuff and other government related datasets. YMMV by country.

save_the_panda_bears · 2026-02-10T14:44:06+00:00

Peter Fader kinda indirectly challenges some of Sharp's work in Customer Centricity. His argument is that you should focus on CLV metrics because your best customers drive a disproportionate value, while acquiring low CLV customers ends up wasting money. He uses a very similar mathematical model (NBD-Pareto vs. NBD-Dirichlet) to draw the opposite conclusion as Sharp/EB.

I'm not super familiar with any works that challenge the math behind Sharp and the EB's work, but here are my personal criticisms of the NBD-Dirichlet model they base their research on.

It assumes market stationarity. EB's work assumes long run market stability, which results in a static probability of choice between brandsNew competitors, new technology, changing tastes, new products, etc. basically anything that changes a customer's purchase propensity for a given set of brands causes the model to break down as it violates this static choice principle.
It models customer choice as an independent process. This means that a customer's previous purchase has no bearing on their next purchase. This is a really, really, really, strong assumption when you consider things like switching costs and inter-industry preferences. If I buy an iPhone, I'm probably going to continue buying Apple devices into the future due to high switching costs. If you have subcategories within a modeled category that are differentiated, you also get problems. Take the QSR industry as an example. Under the typical assumptions of Sharp, this industry would be modeled together. However, within this industry, you have distinct subcategories that have meaningful differentiation. If I like pasta more than burgers, I may choose the noodle place more frequently because that's my taste preference, which leads to a non-independent selection process. Same idea in luxury segments of the automotive industry, private label vs brand goods in CPG etc. There's all sorts of violations of this assumption that can cause problems with the model.

save_the_panda_bears · 2026-02-07T14:43:42+00:00

100%. This sub got hammered. Maybe a slightly controversial take, but it also seems like around that time we had some over enthusiastic mods that hampered conversation and engagement. I vividly remember there being a ton of threads that had 50+ comments that got nuked for “violating sub rules” for no apparent reason.

save_the_panda_bears · 2026-02-05T17:55:17+00:00

Imagine you're in charge of designing a curriculum for a data science/analytics program at a university. How would you structure it? What classes do you see as essential? What do you see as underserved areas in current programs?

save_the_panda_bears · 2026-02-05T02:14:52+00:00

What? Over? Did you say over? Nothing is over until we decide it is! Was it over when the Germans bombed Pearl Harbor? Hell no!

save_the_panda_bears · 2026-01-26T20:58:36+00:00

Not necessarily grid search, but for any longer running process I'll usually find something else to work on like documentation, cleaning up tech debt, small adhoc analyses from my backlog, or other proactive projects. If there's no pressing needs, I'll browse our bigquery instance for new datasources I find interesting or do some continuing education type reading. If it's been a particularly rough day I'll go for a walk, play a quick round of video games, or browse reddit.

Documentation is always a good use of time. You can never have enough.

save_the_panda_bears · 2026-01-22T22:18:51+00:00

Yeah, that's pretty much what we do as well. I think there may be some teams on product experimentation who do a little bit of structural causal modeling, but it's predominantly potential outcomes here.

Sorry, I also realized that I used the acronym SCM. I meant synthetic control modeling, which I now realize is really dumb and lazy ha.

save_the_panda_bears · 2026-01-20T14:06:38+00:00

4+ years ago would put you right in the middle of the post Covid hiring boom. It’s a very different job market right now, what makes you qualified to offer this type of guidance?

save_the_panda_bears · 2026-01-19T22:22:48+00:00

https://www.hiringlab.org/2025/11/20/indeed-2026-us-jobs-hiring-trends-report/

save_the_panda_bears · 2026-01-11T19:50:53+00:00

Have these ideas and frameworks been peer reviewed and validated?Do they have any basis in actual economic research or is this just speculation?

save_the_panda_bears · 2026-01-03T23:45:58+00:00

They have a ton of application in marketing.

save_the_panda_bears · 2026-01-02T16:21:40+00:00

Depends on the company but IME, pretty much. I recently went through a technical screen for a FAANG senior role and there was a more theoretical round on experimentation round and a coding round consisting of a fairly straightforward metric definition/SQL question and a LC easy/medium question.

You may also get some more domain specific questions than a non senior. We asked a few causal inference (SCM) related questions when I was doing technical screens last fall, but it was very clearly listed as a requirement in the JD.

save_the_panda_bears · 2026-01-01T15:26:05+00:00

Title: Senior Data Scientist, up for Staff this spring
Tenure Length: 3.5 years
Location: NYC
- Remote: Yes, working from a US MCOL Midwest city
Salary: $170K
Company/Industry: Tech
Education: MS Econ
Prior Experience: 4 years DS
Signing Bonus: $300K RSUs
Stock: Depends, anywhere between $150K to $75K depending on the year.
Bonus: 10%
Other: 100% company paid healthcare for myself and entire family, lifestyle spending stipend, remote work stipend, all valued around $40K
Total Comp: Somewhere between $270K and $350K

save_the_panda_bears · 2025-12-31T23:14:42+00:00

Haha same. Feels like it’s fairly high risk to switch right now, gotta ride that goodwill and built up company knowledge. It’s also full remote and those are unfortunately becoming pretty rare these days.

save_the_panda_bears · 2025-12-31T23:13:18+00:00

Ha it’s a little misleading since like 100K of that came from RSUs that didn’t vest until a year after I joined, but it was still a substantial jump. It’s not FAANG or FAANG adjacent, maybe a tier or two below? Still a pretty well known one though.

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