LinkedIn Data Scientist Interview Questions
Preparing for LinkedIn Data Scientist interview questions means getting ready for a blend of product-minded analytics, solid SQL and coding skills, and clear storytelling about impact. LinkedIn tends to evaluate candidates on their ability to define and measure product metrics, diagnose metric shifts, design experiments, and translate models into business value, alongside hands-on technical chops like SQL, Python, feature engineering, and basic modeling. You should expect a staged process that begins with a recruiter screen, progresses to a technical phone screen (often SQL and product/analytics questions), and culminates in a loop of interviews that probe modeling, product sense, and behavioral fit. For interview preparation, prioritize realistic practice: sharpen SQL problem-solving with window functions and joins, rehearse product-sense and experiment design scenarios out loud, and be ready to walk through end-to-end modeling choices and tradeoffs. Prepare STAR stories that show ownership and measurable impact, and practice clear, structured explanations of assumptions and limitations. Timebox your prep into focused cycles—technical drilling, case practice, and mock interviews—to build fluency and calm for the real rounds.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Explain Logistic Regression, Backprop, and Adam
Answer the following machine learning fundamentals questions: 1. Logistic regression - Explain how logistic regression works for binary classificat...
Derive mean and variance of x̄
Let \(X_1, X_2, \dots, X_n\) be random variables, and define the sample mean as \(\bar X = \frac{1}{n}\sum_{i=1}^n X_i\). 1. Assume the variables are ...
How do you sample uniformly from an infinite stream?
Coding (Python) — Random sampling from an infinite stream You receive an unbounded/infinite stream of items (e.g., numbers or events). You cannot stor...
Explain variance reduction in random forests
Consider a random forest (or bagged ensemble) that predicts at a fixed input \(x\) by averaging \(B\) tree predictions: \[ \hat f_B(x) = \frac{1}{B}\s...
Find index with positive suffix sums
Given an unsorted integer array \(A\), find an index \(i\) such that every cumulative sum starting from \(i\) to the end of the array is strictly posi...
Compute article-type diversity per user and histogram
You track article views and article metadata. Tables article_views - user_id INT - article_id INT - view_date DATE articles - article_id INT (PK) - ar...
Resolve Simpson’s paradox in email A/B test
Email campaign experiment with Simpson’s paradox A marketing team tests a new email variant B vs control A. The experiment ran for two weeks in two ci...
Handle imbalance, sampling, and overfitting
Practical ML questions (classification and generalization) Answer the following ML engineering/data science questions. A) Class imbalance You’re train...
Sketch distributions and compare mean/median/mode
Distribution sketches and summary-stat comparisons Answer the following conceptual statistics questions. Assume you have a large random sample and you...
Compute each member’s current notification status
You have two tables describing LinkedIn members’ push notification settings. Tables status — each member’s latest setting as of 2020-01-31 - member_id...
Handle imbalance, validate samples, and avoid overfitting
Answer the following applied ML questions. 1) Class imbalance You’re building a binary classifier where positives are rare. - What are practical ways ...
Measure Relevant Feed Success
LinkedIn changes the Home Feed default experience from showing all content to showing only content predicted to be relevant to each viewer. How would ...
Test whether US uploads more videos
You want to evaluate the hypothesis: > “US members upload more videos than non‑US members.” You have: Table: video_posts | column | type | |---|---| |...
Do US members upload more videos than non-US?
You suspect video posting adoption differs between US and international members. Tables members - memberid INT (PK) - country VARCHAR (e.g., 'usa', '...
Find top countries by population per continent
Table world_population - continent VARCHAR - country VARCHAR - population BIGINT Assume each row is a country’s latest population and (continent, coun...
Resolve Simpson’s paradox in A/B email test
A marketing team tests a new email campaign. They run an experiment for two weeks in two cities (SF and NY) comparing Email A vs Email B. They observe...
Choose better bank queue and describe distributions
You are interviewing for a data role and are asked several probability/distribution questions. 1) Bank queue choice (queueing intuition) A bank has 5 ...
Write SQL for rankings, state, and aggregations
You are given several independent datasets. For each dataset, write the required SQL query (ANSI SQL is fine). Assume dates are in UTC and DATE column...
How to diagnose traffic and measure relevance?
You are a data scientist at LinkedIn evaluating two separate Home-page product questions. 1. Home Page to Profile Page traffic declined. The tracked m...
Design a short-video recommendation system
Design a recommendation system for a short-video feed product. Your answer should cover the full pipeline: 1. Objective and labels: Define what the sy...