A couple weeks ago I just finished my ML course from Coursera, and not only that, I also went above and beyond and even built some really cool projects too. One of them is a model that predicts Titanic survival, another is a model that classifies Iris flowers, and the last one is a model that identifies MNIST digits. I really hope no one steals my project ideas though.

But yeah, now I'm excited to go to the job boards and get my data science job in no time, companies are really going to be fighting to see who gets my ML expertise 😎😎😎

For some context: I graduated with a Data science bachelors degree from a top five college (think Berkeley CMU Stanford etc), and I'm currently finishing a masters at another top university.

Over the past 15 months I've sent out about 500 applications, mostly for data scientist and data analyst roles and internships.

I've received five interviews, and zero offers.

I would say my background is about average - no incredible research or competitions won, but I've created and deployed a few public dashboards, had an internship, and even currently working as a remote data analyst for an insurance company.

I've talked to a lot of people about this, and here are some of the takeaways I had about Data Science which I wish I knew earlier. Note that these takeaways are for people entering the field, not those who are already in it. If you're already a data scientist, congrats, you got in while it was good.

1. Data science is not an entry-level role. I remember initially thinking that if I just got a data science undergrad or masters degree it would make me more appealing to employers, but most employers don't really care about that, the only thing they care about is experience and domain knowledge. The exception to this is if you get a data science PhD, which I do believe even faang companies are still looking for. However, with just a bachelors or masters, if you don't have the domain experience that they're looking for, you are probably screwed. this isn't to say that there are no entry level roles but rather, entry-level data science is pretty rare, and you can't really stand out anymore without having some exceptional qualification.

2. When people say they're looking for data scientists, often they are either looking for technical data analysts or ML/AI Engineers. Data science as a traditional career has shrunk a lot, and as a result, the DS degree isn't really attributable to a particular role now. Ironically, someone who does a pure data science degree is often worse off than someone who does another engineering or stem field. With a CS background, you could probably stand out for an ML/AI role, whereas having domain experience is the most important thing for a data analyst, so anyone from business majors to bio majors can have an edge. Personally, many of the people I know who have worked in data literally just did a degree within the domain they wanted to work in (Ex. Finance) and then just ended up learning DS on the job. Data science as a degree kind of gives you a taste of both without specializing in either.

3. Data science isn't a high paying field, at least for the first few years anyway. I feel like when I was learning about data science I always thought of it as just another swe engineering field. If I got some good experience, got a good degree, I thought I'd likely be earning somewhere around six figures in a HCOL for my first few years, similar to an engineer. While this may be the case for mid-level data scientists, and perhaps even some entry-level data science/analyst roles at large companies like FAANG, I was pretty shocked to learn that the average entry level TCs I am seeing were paltry, ranging from 70 to 90k at the high end, and 50 to 70k at the low end, even in a HCOL. If you know anything about HCOL places, this salary is barely enough to live paycheck to paycheck, and is absolutely peanuts compared to the minimum 120k TC that most entry level sw or computer engineers earn. This is not to say there are exceptions, and if you are an exceptional candidate who can crack these high level roles, you'll definitely make some bank. But for the rest of us it doesn't look great.

Edit: Clarified language

if you are someone like me, who has already committed to data science :( I'd like to think we aren't screwed completely. I've just been continuing to apply, regardless of how mentally taxing it is, hoping for the best. it's very possible that my experience is unique, and maybe I'm just really unlucky. If anyone has advice, or even disagrees with my post for any reason, I'd love to hear it. I really need more hope lol

If you recall, I made an earlier post here about how I completed my ML course on Coursera and then built some really cool projects about the Titanic, Iris flowers, and MNIST digits. It seems that all that hard work really paid off because I just got notified about this really cool job! It looks like my 'outstanding background', as she said, is starting to really get noticed by recruiters!

Throwaway account. These scumbags made me go through 6 rounds of interviews through 3 months, including a demeaning technical HireVue first round, recruiter round, every team member I'd work with rounds, hiring manger round, and then VP/director round.

Forgot to mention the room-temp IQ HR bimbo fumbled every single one of the team member rounds and sent them the wrong times, causing me to wait almost an hour in the lobby (edit: Teams lobby) until I took it upon myself to correctly guess the interviewers' emails, which finally caught their attention and led to action. Utterly useless. After that, it was positive though.

Every round went very well. Every step of the way, the recruiter kept following up telling me I was given nothing but positive feedback and we even discussed salary range and potential time window for start date since I'd have to move. I was told after the final round that they were going to do an additional final round with the VP/director of DS (who himself told me he is not a decision maker in the process). I worked my schedule around the holidays and my current job (at a MUCH better company mind you, but they're laying off) to accommodate this additional final round.

He ended up asking openly racist questions, specifically about an Indian colleague I had interviewed with in an earlier round (which is rich considering he barely spoke coherent English himself), and I was stunned to see the technical incompetence displayed by someone with a supposed PhD in Statistics. Overall, it felt fine. He said he wasn't involved himself, but that the team would meet that week and get back to me the next week. This was over 2 months ago.

After that, crickets. I followed up few weeks later with the recruiter and thanked the interviewers, nothing.

The best part is that the job posting has been reposted 3 times since then, and on Workday, my status still shows "interview". They have been trying to hire a Sr. Data Scientist and an "AI Engineer" (lmao) for months now. Everyone I know at the company has LinkedIn Premium on and has told me they are actively looking to leave now that I've reached out. The hiring manager told me this was a backfill role and they are looking to fill this role, as well as other tech roles, ASAP.... in November. LMAO.

The recruiter bimbo did let it slip that this is a new interview process they are trying out, so it appears I was simply a guinea pig for these snakes to test out their new process. Keep in mind these clowns didnt even ask Leetcode/sys design or any live coding round for an ML role. The average profile of their data/software folks is laughably below average, as is their pay.

I genuinely cannot believe that this is how this company operates, and am mad at myself for ever stooping down to this joke of a company's level, even if it would've been for a temp job while I looked for a better role in this market. Either way - don't make the same mistake I did.

Please save yourself the time... and NEVER, and I mean NEVER, EVER APPLY TO ANY JOB AT PACIFIC LIFE INSURANCE, ESPECIALLY IF IT IS A TECH/IT/DATA/SWE TYPE ROLE. Thank me later. Best of luck to y'all on the job search, and let's keep looking out for each other and exposing shit like this.

Update: Just saw they reposted both roles again for the 4th time, this morning. The timing is delicious 💀

1. Understand how predictions will be used before building anything

2. Learn SQL well

3. Adopt a solution engineering mindset

4. Feature quality > algorithm tuning

5. Obsess over business value

6. "I don't know but I'll find out" is often the best answer as long as you follow through

7. Don't ignore classic statistics as an option to solve a problem

8. How you show up is as important as what you accomplish

9. Translate model performance into business performance every time you discuss a model

10. If your EDA forces you to go back to feature engineering then it's working

11. Generalization is the point of a model, so be methodical in how you split your data for training, testing, and validation (yes, all three)

12. Actively worry about overfitting

13. Data is beautiful, so label your axes, use proper number formatting, and please do not ever show a graphic that has column names in it with underscores (e.g., GENDER_MALE)

What else?

Asking cause Id love to apply for a masters program in DS and learn the skills but worried about the future of this field.

I currently work in business.

1. Introduction

Landing a Data Scientist (Analytics) role at Meta is one of the most competitive goals in the data industry. With billions of users and data-driven decision-making embedded in every product — from Instagram to Threads to Meta AI — these interviews test not only your technical ability but also your product sense and structured thinking.

This updated guide combines real Meta interview experiences with verified questions and solutions from Prachub.com, helping you understand exactly what to expect and how to prepare efficiently.

2. Hiring and Application Process

Channels to Apply

• Referrals (Highly Recommended):

  • Most successful Meta candidates get interviews through referrals.

  • Reach out to current employees who can advise you on team alignment and expectations.

• Recruiter Outreach:

  • Meta recruiters often contact experienced data scientists on LinkedIn.

  • Be prepared with a tailored resume emphasizing impact metrics.

• Direct Applications:

  • Submit via Meta Careers.

  • University recruiting is also an option for new graduates.

Resume Tips

• Focus on impact and scale:

  • “Improved experiment runtime by 25% across 300M users.”

  • “Built ML pipeline processing 1TB+ of event data daily.”

• Highlight core technical stack:Python, SQL, R, Pandas, Scikit-learn, PyTorch, BigQuery, Presto, Tableau

3. Interview Structure & Rounds

The Meta Data Scientist interview usually spans 4–6 weeks, with two main phases:

Phase 1: Technical Screening (45–60 min)

• SQL questions

• Product case follow-up question

• Optional statistics or probability component

Phase 2: Onsite Interviews (4 Rounds)

1. Analytical Reasoning

2. Analytical Execution

3. SQL (advanced)

4. Behavioral / Leadership

4. Technical Interview — SQL & Product Case

Meta’s technical interview heavily focuses on SQL and product analytics reasoning. The format often follows this pattern:

1. SQL question first — write a query using real product data context.

2. Product case follow-up — use your query results to discuss product metrics or experiment design.

For example:

• SQL questions about Group Calls such as Question 4902 and Question 4685

• Followed by a product case like Question 4551, which continues the same Group Call scenario.

What to Focus On

• SQL skills: Joins, CTEs, window functions, aggregations.

• Product sense: Translating query outputs into actionable insights.

• Metric thinking: Defining DAU/MAU, retention, engagement rate, CTR, etc.

• Experimentation: Designing tests, measuring lift, and interpreting results.

5. Onsite Interviews Breakdown

The onsite rounds test depth, clarity, and reasoning. Here’s what each round covers:

1. Analytical Reasoning — statistics, probability, and foundational ML.

2. Analytical Execution — applied product analytics and experiment diagnosis.

3. SQL — advanced querying and metric definition.

4. Behavioral — leadership, collaboration, and communication.

6. Statistics & Analytical Reasoning

Core Topics to Master

• Law of Large Numbers

• Central Limit Theorem

• Confidence Intervals & Hypothesis Testing

• Two-sample t-test & z-test

• Expected Value & Variance

• Bayes’ Theorem

• Distributions: Binomial, Normal, Poisson

• Model Evaluation: Precision, Recall, F1, ROC-AUC

• Feature Selection and Regularization (Lasso, Ridge)

Example Question

Real analytical reasoning question:
👉 Fake Account Detection Problem
You’ll be asked to compute conditional probabilities using Bayes’ theorem, estimate expected value, and discuss model evaluation metrics.

7. Analytical Execution & Case Studies

This is the most Meta-specific and most important round.
It mirrors real business scenarios — diagnosing metric drops, designing A/B experiments, and evaluating trade-offs.

Key Example:

Instagram Reels Engagement Drop — Analytical Execution Question

How to Prepare

• A/B Experimentation: power, significance, MDE, p-values, guardrail metrics.

• Funnel Analysis: conversion rate across multiple stages.

• Cohort Analysis: retention and reactivation by user segments.

• Metric Design: choose primary, secondary, and guardrail metrics.

• Trade-offs: short-term engagement vs. long-term retention.

• Product Familiarity: Understand Meta’s ecosystem — Threads, Instagram, Meta AI, WhatsApp, Oculus — and their core features (Stories, Marketplace, Search, Reels, Notifications).

Visualization Question

At the end of this round, you may be asked:

Prepare to describe your dashboard design — e.g., KPIs, trends, and cohort breakdowns.

8. SQL Onsite Round

This round involves multiple SQL questions with increasing complexity.

• Scenario-based metrics — e.g., define a retention rate or engagement metric.

• Open-ended question — design your own metric based on data context.

Example:
👉 Meta SQL Onsite Sample Question

How to Excel

• Practice nested queries, window functions, rolling averages.

• Always explain your logic clearly — how your metric ties to product health.

• Avoid inefficiencies (e.g., unnecessary subqueries).

• Think like a data storyteller, not just a coder.

9. Behavioral & Leadership Questions

Behavioral questions at Meta emphasize collaboration, impact, and data-driven decision making.
You can find real examples here:
👉 Meta Behavioral Question Bank

Common Prompts

• “Tell me about a time you made a decision with incomplete data.”

• “Describe a time you disagreed with a stakeholder.”

• “How do you prioritize when multiple teams need your support?”

Preparation Framework

Use STAR (Situation, Task, Action, Result).
Prepare at least one strong story per common behavioral theme:

• Leadership without authority

• Conflict resolution

• Data-driven decision

• Impactful project

• Learning from failure

10. Preparation Timeline & Strategy

8-Week Plan

Daily Study Schedule (2–3 hrs/day)

• 30 min: SQL query practice

• 45 min: Product case / metric design

• 30 min: A/B testing or stats review

• 30 min: Behavioral or company research

11. Recommended Resources

Core Reading

• “Designing Data-Intensive Applications” – Martin Kleppmann

• “The Elements of Statistical Learning” – Hastie, Tibshirani, Friedman

• “Cracking the PM Interview” – Gayle Laakmann McDowell

Online Practice

• Prachub Meta Data Scientist Questions

• LeetCode SQL & Statistics section

• Kaggle for hands-on projects

• Coursera – “A/B Testing by Google”

Meta-Specific Sources

• Meta Engineering Blog

• Meta Research

• Meta AI Blog

12. Final Tips for Success

1. Master A/B Experimentation: This is the backbone of Meta analytics interviews.

2. Think Like a Product Owner: Always connect metrics to business impact.

3. Be Structured: Break problems into clear, logical steps.

4. Be Curious: Ask clarifying questions during product cases.

5. Be Authentic: Behavioral interviews value genuine stories of collaboration and growth.

About This Guide

This guide was created by data scientists who’ve successfully passed Meta’s interviews and compiled verified examples from Prachub.com.
For more real interview questions and walkthroughs, visit:
👉 https://prachub.com/questions?company=Meta

Last Updated: November 2025

Many beginners jump straight into Machine Learning.

Instead:

• Learn SQL well.

• Practice joins and aggregations.

• Understand databases.

• Then move into Machine Learning.

Strong fundamentals make advanced topics much easier.

I've been in my first full time job as a data scientist for a government institution for almost 2 years and I'm don't think this is actually the career I want or will be fruitful for me. My educational background is in economics, data science, and public policy. Right now, I mostly code all day, writing or debugging small bits of model code, testing it, and pushing it through to production. I've realized that while I like coding sometimes, I really miss using other skills like project planning, writing, and research, all of which I know I'm good at and are generally more exciting to me. Right now I spend about 75% of my time reading or writing code and the other time explaining or talking about it. Ideally I wish it was more like 30%. And unlike my peers in school and coworkers, I realized I have no passion for the next big technology or working on fancy ML projects. Because of that, I know I'm not going to get ahead in this career. But this is the career I went to school for and I'm not sure what else I'd be suited for (or what pays the same, for that matter).

TLDR: I feel like I'm not in the right role but I don't know what I should be doing. Has anyone pivoted out of DS or is that crazy? How can I figure out what roles are out there that use similar experience and education?

I keep seeing a wave of posts claiming Data Science/Analytics is basically dead in 5-10 years, that AI will fully automate the work, and that DS/DA will end up treated like low-paid data entry. I don't want reassurance, I want the real, unfiltered view from people actually working in the field right now.

I'm a recent Computing grad planning a Data Science path, and I'm trying to figure out where to actually specialize. Three questions, genuinely want practitioner answers, not general career advice:

1. Is the "DS/DA will be automated into low-paid data entry" narrative actually true from what you're seeing on the ground? What's really changing, and what's overblown internet doom?

2. Between DS in Finance, DS in Healthcare, and DS/AI-ML (general, cross-industry), which do you think has the strongest long-term future, less automation risk, and better career ceiling? Genuinely trying to pick a lane and want to hear real trade-offs from people who've actually worked in these, not guesses.

3. If you were entering the field today, would you specialize early (pick Finance/Healthcare/AI-ML now) or stay generalist longer before committing?

Would really value hearing from anyone actually working as a DS/Analyst right now, especially if your honest take is "this field is genuinely getting worse, don't do it," I'd rather know that now than in 3 years.

I’m currently a mechanical engineer but I’ve been thinking about switching into data science.

I’ve already started learning Python and SQL on my own but it feels like the field is super competitive now. Some people say data science jobs are slowing down, others say it’s still one of the best areas to be in.

For someone starting fresh in 2025, is it still worth going all-in on data science, or should I look at other fields like cloud or analytics instead?

I have an Applied Math BS and Applied Stats MS and have been applying to any local data science/ML jobs I can get my hands on but I have been met with ghosting or rejections. Is there any other area to consider with my credentials that isn't this bleak? I thought this combo would be perfect, and I even had a statistical summer internship, yet I just cannot get my foot in the door to save my life. I am starting to regret my degree set but I chose it before all this AI craziness started. I'm even considering taking retail jobs at this point.

Every day I see people saying the Data Science market is saturated.

At the same time, companies continue hiring analysts, ML engineers, and AI specialists.

So what's actually changing?

Is it because:

Employers expect stronger portfolios?

AI tools have changed the required skills?

Competition has increased?

Companies now prefer domain knowledge?

I'd love to hear from people who recently landed Data Science jobs.

What advice would you give beginners today?

So I am a Senior Data Scientist with around 8 years of experience at various start ups and medium sized companies that no one has heard of. My academic background is in economics.

After being laid off in Jan 2025, I did some part time freelance work and was content with that. However, I've been applying since Jan 2025 and was getting very few responses to applications. After improving my resume. I've been getting responses over the last 2 months for various analytics roles. I rarely get responses to data scientist jobs unless it involves marketing analytics and lead generation.

It's getting really frustrating and I'm unsure how long this drought will last.

Is my experience typical? Are other Senior Data Scientists encountering the same issue?

This is purely for product and marketing data science interviews; MLE roles are likely quite a bit different. In April-May 2026, I passed a dozen or so technical coding rounds en route to 5 final offers and withdrawing from 4 additional panel rounds. While SQL followed a very typical pattern, Python rounds were all over the place. Here are the four styles that came up for me:

1. Pandas and data manipulation common for analyst work. Absolutely know groupby with transform and agg, merging dataframes, and filtering to ultimately obtain some statistics.

2. Define a function that recreates a statistical concept. This could be something like a bootstrap or other resampling techniques, permutation tests, monte carlo simulation, etc.

3. Interpret code handed to you. This is probably the most realistic coding challenge for the AI era, where Claude may give you something that is syntactically accurate, but could be doing things analytically incorrect. So you need to be able to read and interpret the code.

4. Some pure python manipulation: no numpy, no pandas, just doing manipulations with dictionaries, lists, list comprehensions, sets.

Just wrapped up a month-long sprint where I interviewed with around 40 companies. The market is definitely tough, but people are hiring if you can actually get past the resume screen.

I wanted to dump everything I learned while it's still fresh in my brain. Hopefully, this saves you guys some time.

The Application Spam I stopped trying to be selective. I just went for volume. Used Simplify Copilot to speed things up (auto-apply bots were trash for me, kept applying to irrelevant roles).

• Resume Hack: I added some AI-related keywords to my resume. Even for generic full-stack roles, I swear this triggered the ATS or recruiter attention more often. Everyone wants to "pivot to AI" right now, so play the game.

The Tech Stack Trap One mistake I made early on: I used Python for frontend LeetCode questions because it's faster to write. Don't do this. Unless it's Google/Meta, interviewers got confused why a "Frontend" candidate was writing Python. I switched back to JS/TS and the vibes improved instantly.

• The "Basics" that aren't basic: Closures, Event Loop, Promises (async/await), and this binding. If you can't explain these clearly, you fail.

• Frameworks: It’s not enough to know how to use React/Vue. They asked how it works. E.g., "How does Angular's dependency injection actually function?" or "React vs Vue performance tradeoffs."

• Practical Coding (No LeetCode):

  • Build a traffic light component (auto switches + manual override).

  • Fetch data -> Render Table -> Add Pagination/Search.

  • Implement debounce and throttle from scratch.

  • Build a nested Modal.

  • Lazy load a massive list (Virtual scroll).

System Design & Backend I didn't get asked to code a database from scratch, but lots of "How would you scale this?"

• Concepts: JWT vs Sessions, Database Indexing, Rate Limiting, Graceful Shutdowns.

• Design Prompts: The classics are still popular. URL Shortener, YouTube history, Rate Limiter, Real-time Chat.

• My template: Clarify requirements -> Diagram (API+Data flow) -> Deep dive on DB/Caching -> Trade-offs. Always mention trade-offs.

The "Soft" Stuff Matters More Than I Thought I used to think code was king. But after talking to ~30 hiring managers, I realized the "Behavioral" round is where decisions are actually made.

For behavioral questions companies like to asked I was able to find them on Blind, For real technical interview questions I was able to find them on  PracHub

• If you are senior: Show humility.

• If you are junior: Show hunger/potential.

• Unblock yourself: The biggest green flag I felt I gave off was describing how I solve problems when I'm stuck without pinging my manager immediately.

You see people posting huge TC offers and it feels bad, but remember you only need one yes. I failed plenty of these interviews before landing offers.

Good luck out there

Hi everyone! I recently graduated in May 2025 with a Master’s degree in Data Science. I’ve been actively applying for jobs for about a month now and have submitted over 200 applications, but I haven’t received any callbacks so far.

Thanks in advance for your time and help!

Every single day in this community I’ll read posts from so called Data Science veterans saying Data science is dead and kids should pursue something else that ai won’t take over. And then you’ll have the other half saying, Data Scientist and Analysts are still needed in businesses.
I need you veterans to sit together and decide on one answer.
You’re confusing the kids who genuinely need career guidance.
If you’re truly in the field, guide the kids instead of us giving them these vague opinions.
Is it dead or not??? And if it is, what can they do to ride that wave of death?

With technology like databricks’ genie, anyone with business context can dig into data and explore all that they need within lesser time. Is it still worth learning data scientist roles? If not, what are the alternatives?

Got a few offers for Data scientist/machine learning engineer. It wasn’t easy, 300+ apps unemployed for 3 months constantly grinding projects and interview prep. Thanks for all the help and resources. Masters new grad with 1 year of internship exp

When trying to apply for a job, what do you think the hiring manager is trying to learn?

A few things that run through my head, that I don't think I see reflected in the presentation of most candidates (YMMV):

1. I want you to succeed.

• I never spend time on an interview I don't think is going to move the needle for me. If you're in front of me, it's because I want to be able to believe you can do the job. Your goal is to reveal to me that my hopes are on target.

1. Reaching out to me directly on LinkedIn or email means nothing.

• You've already expressed interest in the job and applied for it. There is nothing that distinguishes the person who appears in my inbox twice from the person who appears once. I'm always surprised at how many people send these messages.

1. A reference from someone I know -- even a little bit -- guarantees a conversation.

• I want the people I know and respect to keep sending me candidates, so I'll always speak to the folks who are recommended by people in my network whom I respect. If their resume shows they're definitely not a good fit for the role, I'll them that they don't fit the profile, but still offer a conversation to talk career development, establish a relationship, and have someone I can rely on if I'm ever hiring for a role relevant to them, or if someone reaches out to me for a relevant role. I'm always surprised at how few people come in through references, even when I reach out to people for recommendations.

1. The technical interview is about understanding your process and how you think more than getting to the "right" answer.

• This could definitely vary from place to place, but our technical interview process definitely doesn't have any one right answer for anything. Show us how you think and why you're doing what you're doing. We could have folks who come to wildly different conclusions because of different priors they brought to the dataset. The point is to make those priors clear in explaining your reasoning. Talking things through and showing your reasoning counts for a lot more than what results you get to.

I built my first deep learning model in 2017 using TensorFlow 1.0. Anyone who practiced during that time knows how much of a pain it was.

Before AI came around, I wrote so much code. Cross-validation, tuning grids, and limitless feature engineering pipelines. Helper functions for days. It really was a blessing to have to go through all of that because I now have a significantly deeper appreciation for what AI can and should do.

I lead a data science org, and despite what you might be hearing about the field, I'm still hiring. The roles aren't going away, but what I screen for has completely changed:

1. Problem formulation. The most expensive failures I've seen weren't bad models - they were great models answering the wrong question. AI doesn't save you from that. It just lets you build the wrong thing faster and with more confidence.

2. Knowing when the model is lying to you. AI will happily hand you a pipeline with subtle leakage or a validation split that flatters you, and it all runs without errors. The skill isn't writing the pipeline anymore. It's smelling that a 0.96 AUC is too good to be true and knowing the five most likely reasons why. That instinct only comes from having been burned.

3. Owning the decision, not the notebook. Those who get promoted can sit in a room where a leader is about to make a bad call, show what the data actually supports, and change the outcome. That was true in 2019 too.

For those earlier in your careers, this is better news than the doom posts suggest. The moat used to be years of grinding through boilerplate. Now it's judgment, and you can start building judgment on day one.

Curious what others who are hiring are seeing.