Brooo the training module started and it never said when it ended and I missed on a lot of time in Redrock study, it also glitched and took me back to the menu and showed start task but it took away all the time. I did well on the other two I believe. How fucked am I? lol I completed the redrock but I wouldn’t say it was the best.
#mckinseysolve2026
Hi everyone,
The mods kindly approved this AMA, so I thought I’d introduce myself briefly.
I’m Florian. I spent around five years at McKinsey and now help candidates prepare for the recruiting process and break into the firm.
My own path actually started with failing my first McKinsey interview for an internship. That failure motivated me to approach preparation differently, and I eventually joined the firm full-time on my second try.
Back then, I spent a lot of time looking at the whole case prep landscape and became particularly interested in frameworks and structuring, and why so many candidates struggle with them, why memorized frameworks often break down, and what actually works instead.
More or less by accident, that became the topic that got me into this whole field.
Interestingly, the prep landscape today is still quite similar in many ways. I've followed the industry and how McKinsey recruiting has evolved very closely ever since.
Happy to answer questions about McKinsey recruiting, Solve, case interviews, frameworks and structuring, PEI, final rounds, life at the firm, or consulting prep more broadly.
I’ll answer as openly as I can.
For verification, here is my LinkedIn: https://www.linkedin.com/in/dr-florian-smeritschnig-b5b9661a/
Ask me anything!
I applied last week and got this email today, stupid question but does everyone get invited to this game or is it send to only those who pass the initial review? Im not gonna waste 2hrs of my day on this if everyone gets invited to play this lmao
I wasn't able to complete the third site in the Sea Wolf assessment before time ran out. How much do you think this will affect my score?
Has anyone else been in the same situation and still made it to the next round?
I applied at London office for a JA role. CV passed the pre-screening stage. I would say my CV is solid, I have been quoted in newspapers, currently in strategy consulting. Even won strategy consultation projects for my current firm.
Just wondering what are they even looking for at McKinsey.
Anyone willing to share the fee for any practice website simulator for mckinsey solve game ?
Hey all, I recently put together a free simulator for anyone preparing for McKinsey's Solve exam. Know there are a lot of paid prep materials and half-baked vibe-coded products floating around Reddit so want to preface that this is 100% free and just a fun side project for me, so very much welcome any feedback to make it better.
I spent a lot of time trying to make the UI feel as close as possible to what you'll actually see on the McKinsey assessment based directly on my experience. Even if you don't use it to practice, I think it's a helpful way to get familiar with the interface before test day.
If anyone gives it a try, I'd genuinely love any feedback. I'm always looking for ways to improve it and make it more useful for future candidates. I also built simulators for Sea Wolf and Sustainable Futures Lab and am giving away some early access keys for free as I continue to develop those - comment "McKinsey" and send me a dm if interested!
I went through a bunch of websites that offer 'free' trials however they never tell me the final score or where I went wrong. some of them even cut out like site 1 of the sea wolf game. are there any free resources that take you to the end?
Tryna cope 😅
Hi all, I applied to a Junior associate role within Tech and AI in London. I got a request to complete the Solve assessment. Does every applicant gets this or is this after resumes have been screened?
Thanks!
I'm a business/quant student in continental Europe, finishing my bachelor's and heading into an MSc in Financial Engineering next year. Applied on a bit of a long shot — I'm not a pure CS or stats major — and somehow got the HackerRank invite for the Data Scientist Intern position.
I genuinely feel like if I can land this, it sets the trajectory for the rest of my early career. I'm willing to clear my schedule and go all-in on prep for every round.
From the prep booklet they sent, it looks like the process is:
**Pre-stage:** HackerRank (modelling + technical knowledge questions)
**Round 1:** Experience Interview + Business Case, AND a Technical Experience Interview + Practical Skills Assessment (pair programming in Python on HackerRank)
**Round 2:** Another Experience Interview + Business Case
Right now I'm focused on Round 0 – the HackerRank OA. For those who've been through it or something similar:
**1. What specific tips do you have for the OA?**
- How long was it really? The booklet says anywhere from 10 to 90 min.
- Was it more coding/modelling or more MCQ-style technical knowledge?
- Did you get a mix of both or just one type?
- Any gotchas or things that caught you off guard?
**2. What should I be preparing?**
- The job description mentions pandas, NumPy, scikit-learn, EDA, RAG/LLM use cases, and statistical analysis. Is that actually what shows up on the test?
- Should I focus more on ML fundamentals (bias-variance, cross-validation, metrics) or on actual coding tasks (data wrangling, feature engineering, building a baseline model)?
- Any SQL or stats theory questions?
**3. Best study materials / resources?**
- Is StrataScratch or LeetCode more relevant for this type of DS assessment?
- Any specific Kaggle notebooks or courses that mirror the format?
- Textbook or cheat sheet recommendations for brushing up on sklearn workflows, model evaluation, probability/stats?
**4. Things I might be forgetting?**
- Did anyone get questions on experiment design (A/B testing, hypothesis testing)?
- Data visualization or interpretation questions?
- Anything on GenAI / RAG / LLM concepts given that's in the job description?
- Time series, NLP, or deep learning – or is it strictly classical ML + stats?
- How important is writing clean, well-structured code vs. just getting the right answer?
For context: I have working knowledge of Python (pandas, sklearn, numpy), R, SQL, and some experience with API-based data pipelines and quant simulations. My programme has solid stats and econometrics foundations, but my DSA fundamentals are decent, not competition-level.
Any advice – even just "focus on X, ignore Y" – would be massively appreciated. Happy to pay it forward and post my experience after the process.
Thanks in advance 🙏