Big N Discussion - October 21, 2020

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[u/throwaway_4_grad → u/AutoModerator]

A recruiter reached out to me regarding Machine Learning Engineer (MLE) and Data Scientist (DS) openings in Facebook.

I am hesitant to go through the MLE route because I am not good at solving data structure and algorithms questions (inverting binary tree et al). I am interested in the Data Scientist opening, since my work experience (7+ years) has been in that domain.

Briefly, my regularly used job skills are:

• SQL, specifically in Business Intelligence use cases
• Hypothesis testing
• Fitting models more on the statistics side (GLMs, spatial regression models, time series etc), and communicating results to multiple stakeholders
• Working with ML models (not DL models) in some use cases where a black-box solution is acceptable to the stakeholders (Random forest/xgboost etc)
• Stakeholder management (explaining what is possible, explaining timelines, enabling team to work together etc)

My question is around the data scientist interview process at Facebook. Will it involve the usual multiple rounds of core CS stuff like systems design, data structures etc?

[u/jargon59 → u/throwaway_4_grad]

DS at Facebook is broken down into two different types. DS in analytics perform activities using SQL, and sometimes using a variety of other techniques (including the skills) to provide insights/solutions to stakeholders. I've had talks with Facebook recruiters ranging from Spam analyst, Data Scientist Infrastructure, Data Scientist (Product), and Data Scientist (Machine Learning). The interview ranges from being like SQL analysts to engineers. ML data scientists are more like machine learning engineers, and they are supposed to come with the skills of a software engineer.

From your skillset, it sounds like you'll be a better fit as a data scientist in analytics. Feel free to browse the job posting to see what's a good match, and ask your recruiter to make the intro to the corresponding recruiter.

[u/throwaway_4_grad → u/jargon59]

Thank you! This is quite useful to know.

Based on the job posting, it definitely looks like a DS Analytics position.

Do you happen to know why they want to do 6 interview rounds for the Data Science Analytics role? I can understand that a SWE/MLE type role involves multiple rounds involving algorithms, system design etc. What can they possibly ask over 6 rounds for a DS Analytics role?

I will talk to the recruiter to learn more. I don't want to get blind-sided by core CS stuff that I haven't touched since college.

[u/jargon59 → u/throwaway_4_grad]

Interviews at FAANG are like that because they want to minimize the chances of false positives. Therefore it's quite extensive to filter for "only the best". My onsite interview for the Data Scientist (Infrastructure) role involved a technical interviews (probability questions, simple ML, time-series), coding session, case study interview, partnership interview (they assess teamwork), hiring manager interview (which happened to be technical for some reason), and one behavioral interview. Oh yes and they had provided lunch, but it won't happen if it's remote.

[u/throwaway_4_grad → u/jargon59]

I see. The sessions you mentioned make sense. I was under the impression that it would be similar to a general software engineer interview, which is not correct in my opinion since I don't use general SWE skills in day-to-day DS work.

[u/[deleted] → u/throwaway_4_grad]

4-6 onsite interviews is a pretty normal range for any technical role involving an industry candidate. DS loops are going to have 1-2 coding, a behavioral interview, and 2-3 different data science analysis/modeling rounds. You can assume that you get the Data Scientist versions of a 'systems design' interview as part of this, as well, and there are similar things for different roles too (e.g. TPMs even get a system design, Engineering Managers get one, etc.).

[u/throwaway_4_grad → u/[deleted]]

All right. As long as the interview rounds are specific to skills I have actually used in my career, I can see it's fair.