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BCG X Data Scientist R2 Prep Tips

Hello!

I’m preparing for Round 2 of BCG X (US) for the Data Scientist role. It includes 3 technical/data science case interviews plus fit/behavioral questions.

My feedback from Round 1 was very positive in terms of my case interview structuring and solving top-down, except that I’m a new grad without prior full-time work experience. To combat this, I plan to sound more polished to show expertise. I’m confident in ML and DS fundamentals (daily revision is ongoing to polish my terminology use), but I’m worried about handling unstructured case interviews, especially in unfamiliar industries.

I need tips/insights anyone could provide to help me prep strategically. Would appreciate:

  • Tips on building structured approaches to open-ended DS/ML cases
  • What frameworks have worked well for others in DS cases
  • Affordable mock interview partners (especially for data science-specific case interviews, not just general consulting cases)
  • Should I expect Deep Learning/NLP/Gen AI deep dives?

If anyone is also preparing for BCG/DS cases, happy to exchange mocks!

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Top answer
on Jul 23, 2025

Hi Kira, I'm also looking for tech case interview partners. Would you like to practice with me? =) Looking for sometime this week.

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on Jul 23, 2025
Yes! Thats perfect. Thank you! How could I message you privately? I do not have premium
on Jul 23, 2025
Great thanks, have sent you a meeting proposal. please feel free to propose another time if its better for you
Alessa
Coach
on Jul 23, 2025
xMcKinsey & Company | xBCG | xRB | >400 coachings | feel free to schedule an intro call for free

Hey Kira,

Great that you're prepping so strategically already! Here’s how I’d approach your R2 BCG X prep:

For structure in open-ended DS/ML cases, stick to a core breakdown: Business Objective → Clarify Success Metric → Data Availability & Cleaning → Modeling Approach → Evaluation → Actionable Insights. It shows maturity and end-to-end thinking, even if the domain is new.

A simple but solid framework is the CRISP-DM model (cross-industry standard process for data mining) adapted to business lingo. For example, talk about “translating business goals into measurable data problems” instead of “business understanding”.

For unstructured cases in unfamiliar industries, use analogies from domains you do know. Also ask clarification questions out loud, that models expert thinking and keeps you in control.

You probably won’t get full GenAI/Transformer technical deep dives unless you claim to be super specialized in that, but you should be able to discuss tradeoffs of DL vs classical ML, especially on small datasets or constrained infrastructure.

Mocks: Try RocketBlocks DS peers, Exponent for DS coaching, or just post on LinkedIn with clear call for DS-focused case swaps, lots of people are in the same boat now.

You sound well-prepared already, now it’s all about making your thinking sound client-ready and confident.

Let me know if you want to try a DS-style mock or need help structuring one :)

best,
Alessa :)

on Jul 23, 2025
#1 Rated McKinsey Coach | Top MBB Coach | Verifiable success rates

Hi there, 

I recommend you actually try and reach out to a couple of people who are new joiners (<1.5y of tenure) in firms like BCG X and who work in DS. Ask them how they prepared for the interview, what concepts they found most important to focus on. 

Obviously, you shouldn't ask them directly what sort of questions they received in their interview, but rather how they went about their prep. 

That should give you then a more informed perspective on what to focus on.

Best,
Cristian

Evelina
Coach
6 hrs ago
EY-Parthenon (7 years) l BCG offer holder l 97% success rate l 10% off first session l free 15' intro call l LBS

Hi there,

Sounds like you're approaching this really thoughtfully—and great to hear the positive Round 1 feedback! For BCG X Round 2, the key will be combining strong technical thinking with business-driven communication, especially given your new grad status.

Here’s how I’d suggest prepping strategically:

1. Structure for open-ended DS/ML cases:
Use a 4-step structure that works well for unstructured prompts:

  • Clarify the business objective (What is the client trying to solve?)
  • Define the data science approach (What methods could help? Classification vs regression? Supervised vs unsupervised?)
  • Lay out a project roadmap (Steps: data collection → cleaning → feature selection → modeling → evaluation)
  • Highlight trade-offs/risks (Accuracy vs interpretability, timelines, data gaps)

2. Frameworks that work well:

  • CRISP-DM is a strong backbone to adapt for interviews.
  • Problem-Method-Data: Frame the problem → pick the right ML approach → justify with data availability and business context.

3. Should you expect DL/NLP/GenAI deep dives?
Possibly, but only if you indicate strength in those areas or the role calls for it. More likely, you’ll get practical ML problems and be expected to explain model choices and assumptions clearly—not just theory dumps.

4. Mocking partners:
Happy to run through data science-focused case interviews with you—I’ve worked with several BCG X candidates and have cases geared toward that style. You can also check platforms like RocketBlocks or reach out on data-focused Slack groups (e.g., Data Science Society or Insight Fellows alumni if you have access).

With 3 interviews and strong R1 feedback, polishing your communication and practicing some domain-flexibility will go a long way.

Happy to help you prep – feel free to reach out.


Best,
Evelina

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