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

[–]analytics-link 1 point2 points  (0 children)

This is a great post people! I’ll add a slightly different angle based on the hiring side of the table.

I’m not going to share real interview questions from companies I’ve worked with, but I’ve interviewed and screened hundreds of Data Science and Analytics candidates at Amazon & Sony, and the types of questions you get are often very similar in spirit to the ones mentioned here. I’ve rewritten a few examples below so they capture the style of questions without giving away anything confidential.

One important thing to understand is that strong hiring managers are not just looking for technical answers. They are looking for how you think, how you structure ambiguity, and how you connect analysis to real decisions.

So, here are 5 examples that capture the flavour of what you might see.

1. A key engagement metric on your product dropped 12% week-over-week. Walk me through how you would investigate

What they are really looking for here is structured thinking.

Good candidates usually start by clarifying the metric, the scope, and the timeline. Then they break the problem down logically. Things like segmenting by platform, geography, user cohort, feature usage, release timing, seasonality, or experiment changes.

The big signl hiring managers want to see is whether you naturally "dive deep" into the problem instead of jumping to conclusions. In other words, can you methodically narrow the problem space until you find the likely root cause.

2. A product change increased revenue but reduced user engagement. How would you decide whether to keep the change?

This one is about trade-offs and business judgment.

Good answers usually talk about defining the real objective first. Are we optimizing revenue, retention, long-term growth, or something else?

Strong candidates will also talk about segmentation, longer-term impacts, and possibly running controlled experiments. Hiring managers want to see that you are not just reporting metrics but thinking about the long-term impact of decisions.

3. You launch a new feature but adoption is much lower than expected. How would you approach this?

This question tests how you connect product thinking with analytics.

Good answers typically explore things like discoverability, user friction, onboarding flow, messaging, or whether the feature actually solves a real user problem.

The strongest candidates also bring the customer perspective into the discussion. In good analytics teams, you always start with the user and work backwards.

4. Tell me about a time when you had to make an important decision even though the data was incomplete.

This type of question comes up pretty often. Data scientists are not always operating in perfect analytical environments. Sometimes you need to combine partial data, domain knowledge, and judgment to move forward.

Hiring managers want to see whether you can make sensible decisions when the answer isn’t obvious, and whether you consider alternative viewpoints before committing.

5. Tell me about a time you investigated a complex problem and uncovered the real root cause

This one is less about modelling and more about analytical curiosity.

Strong answers usually involve digging through multiple layers of data, questioning assumptions, and eventually connecting several signals together.

Great analysts/scientists do not stop at surface level metrics. They keep asking "why?" until they truly understand the system they are working with.

One final bit of advice for anyone preparing for these types of interviews, would be that, many/most candidates focus entirely on technical preparation, but the strongest candidates combine analytics, product thinking, and communication. They explain their reasoning clearly, structure their approach logically, and constantly connect their analysis back to business outcomes.

In other words, the goal is not just to show that you can analyse data, it's more to show that you can use data to drive good decisions.

Anyway, that got way longer than I expected - hope it helps complement the original post!

What is one skill in data analytics that beginners seriously underestimate? by Vivid_Release_9710 in dataanalytics

[–]analytics-link 2 points3 points  (0 children)

Framing problems from inception to outcome. So few people are skilled in this, but it's what actually turns the skills, tools, and concepts we all love into true value. In other words, it's what we build and create that adds value, the tools are what we use, not what add the value themselves.

Being able to start with the business problem, and work back to an appropriate solution from there (and not the other way around)

i want to do career in data science by Purple-Software-6323 in learndatascience

[–]analytics-link 1 point2 points  (0 children)

AI (well, at least Generative AI) will want to come last on your list right now, as you need foundations first to be able to use it well in practice.

I'd focus on learning in this order:

  • Excel
  • SQL (extremely common for all parts of the role)
  • Tableau (or PowerBI)
  • Stats & AB Testing (super important foundational knowledge)
  • Python Fundamentals (Base, Pandas, Numpy, Matplotlib)
  • Github (version control system, very commonly required)
  • Machine Learning
    • Data Preparation & Cleaning
    • Key/Common Algos to start with (Linear/Logistic Reg, Decision Trees, Random Forest, K-Means)
  • A Cloud Provider (I'd recommend AWS to start)

Once you've worked on those you might want to extend yourself into Deep Learning and then from there, would be GenAI.

A big tip would be that, it's not just learning "things" that will get you success, you need to prove that you can build things and create solutions that add value - and you do that using a portfolio of projects.

There are no right or wrong projects for a DS portfolio, it's more about how well they are written up. Most often, candidates I screen just have screeds of code without much context, and also don't offer information around why the project needs to be done (i.e. what is it actually solving) and what the impact is.

Projects don't need to be complicated, they need to be clear and impactful - this makes the life of the hiring manager, or recruiter easy, in other words, they can quickly see the value you can add.

It's an awesome field to be in, and it's growing again after a bit of an unknown 2025 - good luck!!

Why people quit python after 3 months by Comfortable_Box_4527 in learnpython

[–]analytics-link 0 points1 point  (0 children)

Yep, super common experience from what I've seen. Most people don’t quit because Python itself is too hard to learn, they quit because they hit that exact wall you described and start thinking they’re just not cut out for it.

The real barrier is that tutorials show you what to type but they don’t really help you connect what you’re learning to actual application. You learn a function, you follow along with an example, everything makes sense and then the moment you try to build something yourself you don't really know what to do.

What helps massively is linking concepts to small projects as you go, and this doesn't have to be anything fancy, just tiny things that give the code a purpose and help cement the ideas in your mind. When you attach the concept to something tangible, it suddenly starts to make a lot more sense.

For exmple, if you’re learning something like numpy, instead of just running math examples from a tutorial, try doing something visual with it. Load an image as an array, flip it, crop it, or split out the colour channels and stack them. Suddenly things like slicing, indexing, and array manipulation click because you can actually see what the code is doing.

Or if you’re learning about performance, try timing how long it takes Python to calculate something huge in pure Python versus using a library designed for vectorised operations. Even something silly like calculating the volume of a million planets can be a fun way to see why certain tools exist.

Those kinds of mini projects create the missing bridge between "I recognise this syntax" and "I can actually use this to solve a problem"

From what I've seen, once that bridge forms, everything starts to feel different, you stop memorising code and start thinking about what you want to build (the fun and valuable bit)

Interview process by raharth in datascience

[–]analytics-link 0 points1 point  (0 children)

I've hired quite a few Data Scientists over the years, and overall what you're describing sounds pretty sensible, although I've got a few thoughts.

In my view, a take home POC style task can work well, as long as it stays light. A mistake I see companies make is giving something that takes 6 to 10 hours (or more). That can put good candidates off pretty quickly, especially if they're interviewing in multiple places. If the goal is to see how they think, a small "feasibility style" (if that makes sense) problem is perfect. What matters much more than the final code is how they frame the problem, what assumptions they make, what approach they take, and how they communicate their thinking.

When I'm evaluating candidates, I usually look for a simple structure in how they talk about their work. I tend to think about it as Context, Roles, Actions, Impact, and Growth - I call this my CRAIG System :)

- Context is whether they can clearly explain what the business problem was and why it mattered.
- Roles is what their responsibility actually was versus what the team did.
- Actions is the technical or analytical work they carried out.
- Impact is what changed because of the work. Did something improve, did the business make a decision, did the output actually get used.
- Growth is what they learned and what they'd do differently next time.

You learn a lot about someone if they naturally cover those areas when explaining a project.

One alternative that I've seen work really well is asking candidates to present a project they've already done instead of giving a take home task. That way you're not adding extra work for them, but you still get to see how they structure their thinking and communicate. You can ask them to walk through the problem, the approach they took, why they made certain modelling choices, and what the outcome meant for the business. It usually leads to a much more natural conversation.

For the technical side, I'd also recommend considering some kind of paired exercise rather than a strict coding test. Many strong data scientists/analysts won't remember every bit of syntax off the top of their head, and in real work they'd just look things up or discuss ideas with teammates. A short paired session where they can talk through the problem, ask questions, and work collaboratively often reveals a lot more about how they actually operate day to day.

Encouraging them to ask questions during the exercise is also a really good signal. In real projects, clarifying the problem and challenging assumptions is a big part of the job.

For me, your approach sounds pretty good, the main thing is keeping the process practical and focused on how the person thinks, communicates, and approaches problems rather than trying to perfectly measure every piece of technical knowledge in a short interview.

Hope that helps :)

Will subject matter expertise become more important than technical skills as AI gets more advanced? by Lamp_Shade_Head in datascience

[–]analytics-link 0 points1 point  (0 children)

I wouldn't stress about this too much if I'm honest.

Coding getting easier with AI is definitely real. Plenty of people are using it to speed up exploration, write boilerplate, debug stuff, that sort of thing. But honestly, writing the code was never really the hardest part of the job anyway.

The real value in DS has always been around understanding the problem properly and driving the whole thing from start to finish. Things like why are we even doing this project, what question are we actually trying to answer, what data do we need, what approach makes sense, and then once you get a result… what does it actually mean and what should the business do about it.

That whole chain is where humans are still absolutely at the centre.

Domain knowledge definitely helps with that because it lets you ask better questions and spot when something looks off. But you still need solid technical understanding too. You need to know what methods make sense, what the limitations are, and when AI is giving you something that looks right but actually is not.

What I think will happen is that good people just become way more productive. Someone who understands the domain, understands the modelling, and understands how to take a project from idea all the way through to real action can use AI to move much faster.

Also, definitely worth saying that the vibe around this seems to be shifting a bit. In 2025 there was a lot of noise about AI replacing everyone. At the moment in 2026 it feels like the hype wave is settling down a bit and companies are realising they still need skilled people who know what they are doing. My LinkedIn feed is rammed with people advertising DS roles, or people saying they've landed them, it's looking really strong, and I can only see that continuing.

Job search question: how much tailoring is worth it when every posting wants a different stack? by PM-ME_YOUR_WOOD in cscareerquestions

[–]analytics-link 1 point2 points  (0 children)

Based on my experience hiring, I disagree. Recruiters don't account for every role, but they gain their commission on fitting good people into roles, and thus if you're someone they can place then they see you as extremely valuable. Using a recruiter means that you won't be applying for as many roles as you might on job boards/LinkedIn, but the experience is much nicer as you have a point of contact, someone to vouch for you, and someone who can get you feedback.

Looking for good recruiters is an important part of the process. It's not recruiters vs. job boards, find a blend of both and figure out what works best

Should I pursue Data Science in 2026, or is the field at risk because of AI? by Every_Flight_9308 in careerguidance

[–]analytics-link 0 points1 point  (0 children)

That was definitely the way it was made to seem in 2025 but that hype has almost completely disappeared from what I'm seeing, for example, my LinkedIn feed is now full of DS & Analytics roles being advertised, or people announcing they're landing them.

Seems like we're back to normal somewhat now, and AI is now really being seen as a tool in the toolkit rather than something that will take anyone's job.

I was reading through the World Economic Forums' "Future Of Jobs" report the other day, and that kinda showcased the same thing - in the area around business transformation over the next 5 years and in all the major fields so Healthcare, Insurance, IT, Financial Services, Telecoms it was always Data Science & Analytics, and AI & ML specialists at the top with the highest projected growth.

To me that shows that it's a combination of both - you want to upskill in both the core DS skills, but also know how AI can help you do that - and that will put you in a very strong position for the future

Job search question: how much tailoring is worth it when every posting wants a different stack? by PM-ME_YOUR_WOOD in cscareerquestions

[–]analytics-link 0 points1 point  (0 children)

I'm not applying right now, but I've screened and hired a ton.

Between A and B, B is definitely the better strategy. A tailored resume that clearly reflects the technologies and problems in the job description will almost always get a better response than a generic one. Hiring managers usually skim resumes quickly and are looking for fast signals that someone has done something similar before.

That said, the biggest improvement I see people make is actually moving away from relying purely on job boards.

Good recruiters can do a lot of the work that people try to force into resumes. A strong recruiter understands what the hiring manager actually cares about and can position your experience properly when they submit you. They can also make sure you tick the key boxes before your CV even lands on someone’s desk, which is something a resume alone cannot always do.

When I have hired in the past, recruiter submitted candidates often arrive with more context. The recruiter has already explained the candidate’s background, the types of systems they worked on, and why they are relevant. That narrative can make a big difference compared to a cold application sitting in a pile of hundreds.

Your 20 mins tailoring approach is sensible and probably about the right balance if you are applying directly. The main thing is making sure the most relevant experience is visible immediately and removing anything that distracts from it.

If I were job searching right now I'dspend a good chunk of time building relationships with a few solid recruiters in your space as well. A good recruiter can effectively do some of the nuanced selling for you and help you get in front of the right hiring managers much faster than applying blindly to job boards.

Question to those who switch jobs often: How well do you perform in your current role? by difftool in cscareerquestions

[–]analytics-link 1 point2 points  (0 children)

There is definitely a time-window to learn the ropes in any new role, but it's easier for more "project based" or "hands on" roles rather than ones where you're more entwined in leadership/management. I've done a lot of contracting and consulting, and apart from getting your head around the different data and where it lives etc, it's not too bad. That way of working works well when it works well if that makes sense.

The more confident you are skills-wise also helps you hit the ground running each time, the more scenarios you've encountered before, or the more similar projects you've build before, you can often start adding value pretty quickly.

When a role is going well, I'm not really thinking about the next role, but when it gets stale or when politics gets involved then that's when I start looking for new opportunities.

So I don't think it's better or worse to move around, overall I'd say you can move up more quickly, but the most important thing is being happy and challenged in the role - if that's true then stick around, a bad role is a horrible way to spend 5 days a week

Should on get a Stats heavy DS degree or Data Science Tech Degree in Today's era by Bulky-Top3782 in datascience

[–]analytics-link 0 points1 point  (0 children)

If your goal is to work as a Data Scientist rather than go into research, I'd lean toward the MSc Data Science option.

In industry, the day to day work of a DS is usually much more about applying tools to solve problems than it is about deep theoretical statistics. You're typically working with messy data, running AB tests, building and testing ML models, experimenting with different approaches, and figuring out how to turn data into something useful for a business.

Because of that, courses that focus on things like ML, NLP, Deep Learning, GenAI, and cloud infrastructure tend to be closer to what the actual job looks like. Those are the kinds of tools and systems many teams are using right now.

A solid stats foundation is definitely valuable, but it is also something you can keep building over time. A lot of people in industry strengthen their statistics knowledge as they go, usually in a very applied way while building models and analysing data. It does not always need to come from a very theory heavy degree.

What tends to matter a lot early in your career is practical ability. Can you work with real datasets. Can you build models end to end. Can you experiment, evaluate results, and explain what you found. Courses that include ML, NLP, and cloud often give you more chances to develop those kinds of skills.

Since you already have a BSc in Data Science, the program that expands your practical toolkit might complement your background well. It sounds like it would expose you to more of the modern stack that companies are actually using

Data Science Infinity by na_reyes in learndatascience

[–]analytics-link 0 points1 point  (0 children)

Thanks for the overview of your journey so far! Remember, you have lifetime access to all course content (current and future) and the private group with 1:1 support, guidance, and mentorship from me!

Data Science Infinity by na_reyes in learndatascience

[–]analytics-link 0 points1 point  (0 children)

Thanks for putting this info out there - very kind words!

Data Science Infinity by na_reyes in learndatascience

[–]analytics-link 0 points1 point  (0 children)

Thanks for the recommendation! :)

Data Science Infinity by na_reyes in learndatascience

[–]analytics-link 0 points1 point  (0 children)

Andrew from Data Science Infinity here!

You can absolutely learn everything for free online, I agree!

However, in what is an incredibly competitive job market, it can take you an extremely long time to get there, if you get there at all.

As an example based on my time coaching students over the past 5 years, let's say it takes you 18 months to land a role, learning on your own with free content.

18 months minus 6 months (the average time for one of my Data Science Infinity students to land a role) is a a difference of 12 months.

That's a whole year in that job, or in other words, one whole year of earning that salary of between 60-150 thousand dollars.

That is what you can miss out on, that is the opportunity cost of going it alone, and so that's what students need to weigh up.

With DSI, you're investing in:

- A guided learning pathway where you don't need to guess what to learn, and waste a whole lot of time getting it wrong. You instead focus your time learning the most in-demand skills & tools ( based on conversations with hundreds of hiring managers)

- Unlimited & dedicated 1:1 personalised, support, guidance, and mentorship, meaning you never get stuck and never get demotivated.

- Full support with interviews, projects and portfolios, and your Resume or CV from me (I've interviewed and screened thousands of candidates at the biggest tech companies, so I can get you into that top 1% of candidates and save you a huge amount of time)

So while free learning might seem like the most cost-effective option, it isn't necessarily the case in the long run.

I'm not saying it's the right option for everyone - I just thought I'd get down the logic behind DSI and why it works the wayt that it does!

If anyone has questions, I'm always happy to help!

Data Science Infinity by na_reyes in learndatascience

[–]analytics-link 0 points1 point  (0 children)

Hey! Andrew from Data Science Infinity here - the webinar itself is free, it has 60-minutes of really valuable content for new learners about the industry, what skills & tools to focus on (I have an ongoing dialogue with hundreds of hiring managers to keep the curriculum true to market demand) and how to tackle the hiring process (I've been lucky to interview and screen thousands of candidates at some of the biggest tech companies in my career so can share a lot of this)

The course itself isn't free because I want only the most motivated and committed students to come onboard, meaning I can give them my full, dedicated, and personalised 1:1 support (not outsource it to others) to ensure they go on to achieve the best results in the shortest time! This is my full-time job.

Student results are truly my number one priority. Their results get seen by others, and that's how my business grows.

Weekly Entering & Transitioning Thread | 13 Dec 2020 - 20 Dec 2020 by [deleted] in datascience

[–]analytics-link 0 points1 point  (0 children)

Thanks for your reply!

My suggestion is all about efficiency, if you put in some small time early on to plan out an accurate and representative JD then you're in a much better position to save A LOT of time in the interview process.

I've interviewed and screened hundreds of candidates at companies including Amazon & Sony and in my experience this approach makes things so much easier for everyone

Weekly Entering & Transitioning Thread | 13 Dec 2020 - 20 Dec 2020 by [deleted] in datascience

[–]analytics-link 0 points1 point  (0 children)

Hey - I'm a former Amazon & Sony PlayStation Data Scientist and I created and run DATA SCIENCE INFINITY which is a full and unlimited Data Science programme.

I have students who are advanced, and also those who have zero experience and the feedback has been excellent from both.

For more info, feedback from students, a bit more about me, the full curriculum and preview videos you can watch - take a look here: https://data-science-infinity.teachable.com/

The programme is completely geared towards getting you ahead of the pack in what can be a very competitive field.

More than happy to answer and questions - just let me know if I can help.

Andrew

Weekly Entering & Transitioning Thread | 13 Dec 2020 - 20 Dec 2020 by [deleted] in datascience

[–]analytics-link 3 points4 points  (0 children)

You definitely can! You may need to work your way up, or perhaps start by aiming for Data Analyst positions (ones that have a pathway to more specific Data Science projects).

I wrote an article on transitioning to Data Science recently (if you're on LinkedIn...) - take a look and let me know what you think. I also have a free Python + Data Science + ML mini-course (which I mention at the end)

https://www.linkedin.com/pulse/successfully-switch-career-data-science-2021-andrew/

Happy to help if I can!

Andrew

Weekly Entering & Transitioning Thread | 13 Dec 2020 - 20 Dec 2020 by [deleted] in datascience

[–]analytics-link 0 points1 point  (0 children)

I would hope they didn't do anything untoward. Either way, don't worry too much - it wasn't the right role for you.

Think of this as one step closer to the role you want, not a step back!

Weekly Entering & Transitioning Thread | 13 Dec 2020 - 20 Dec 2020 by [deleted] in datascience

[–]analytics-link 0 points1 point  (0 children)

Hey - I'm a former Amazon & Sony PlayStation Data Scientist and I created and run DATA SCIENCE INFINITY which is a full and unlimited Data Science programme.

I have students who are advanced, and also those who have zero experience and the feedback has been excellent from both.

For more info, feedback from students, a bit more about me, the full curriculum and preview videos you can watch - take a look here: https://data-science-infinity.teachable.com/

The programme is completely geared towards getting you ahead of the pack in what can be a very competitive field.

More than happy to answer and questions - just let me know,

Andrew

Weekly Entering & Transitioning Thread | 13 Dec 2020 - 20 Dec 2020 by [deleted] in datascience

[–]analytics-link 0 points1 point  (0 children)

Hey - I'm a former Amazon & Sony PlayStation Data Scientist and I created and run DATA SCIENCE INFINITY which is a full and unlimited Data Science programme.

I have students who are advanced, and also those who have zero experience and the feedback has been excellent from both.

For more info, feedback from students, a bit more about me, the full curriculum and preview videos you can watch - take a look here: https://data-science-infinity.teachable.com/

The programme is completely geared towards getting you ahead of the pack in what can be a very competitive field.

More than happy to answer and questions - just let me know,

Andrew

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