Acing the ML Portion of McKinsey Data Science Interview
A detailed guide for the what, why, and how of the ML part of consulting interviews
A detailed guide for the what, why, and how of the ML part of consulting interviews
After I wrote about my decision to join McKinsey as well as the most valuable lessons I learned there about data science, I have also shared the reasons for my decision to eventually leave the firm, I have gotten a lot of messages from readers of those articles asking about HOW to prepare for data science interviews for consulting firms. So I thought I would address all of these questions with several articles talking about my consulting interview process and my preparation for it.
My interview with McKinsey was 3 years ago. As the practice evolve, some of the interview processes might have changed a little bit. But the general idea should remain the same and the preparation process shouldn’t differ that much.
So let’s talk about what consulting firms usually look for in candidates and how to prepare for the interviews. There are different sections of the interview — general topics are ML knowledge, case study and cultural fit. I will address them in separate articles to take care of everyone’s attention span. You might be wondering how come SQL and Python (which are commonly tested by other companies for DS interviews) are no on the list. I still strongly recommend knowing at least the basics about SQL and Python before your interview in case the interview format has changed since mine. But McK generally believes that the super technical skills (like coding) can be learned on the job as long as you have some coding experience. What they rather spend time testing are your “soft skills” like ability to learn, structured problem solving and the analytical way of thinking.
Why It Is Tested
In the tech world, usually Machine Learning Engineers (MLE) are the ones that build models and data scientist work mostly on analyses and insight generation. But as a DS consultant, you are viewed as a “full stack” DS, meaning you need to be able to cover things from data pipelining to ML modeling, all the way to insights generation and “storytelling” with data.
So consulting firms want to make sure that you have enough ML knowledge to work on or even lead modeling projects.
How It Is Tested
Like I mentioned in my interview series about ML, there are generally two ways to test ML knowledge I haven seen — resume based or theory based.
Most consulting interviews are resume based from what I have heard (and experienced). That means two things — you need to have some modeling experience and you need to know how to talk about it.
How to Prepare for It
In order to be able to talk about modeling experience, you need to, well, have some modeling experience. You can get modeling experience through your current work by getting on modeling-intensive projects; if that’s not an option, you can always utilize websites like Kaggle to get some modeling experience through side projects.
When it come to learning the basic theoretical ML knowledge. The best book to use in my opinion is The Analytics Edge written by MIT operations research professor Dimitris Bertsimas.
It covers things from basic concepts like linear regression, CART model all the way to more complicated models like the random forest. It doesn’t cover the super advanced models like neural networks, reinforcement learning etc. But based on my experience, I rarely used those models in my consulting life, so I will be surprised if consulting interviews put a lot of weight on those.
If I were to re-read this book from scratch for an interview prep, I would start from Chapter VIII, where each model is dissected and explained in details, before moving onto some of the previous chapters which demonstrate case studies of the models.
If you lack model-building experience outside of academia and Kaggle environment, you might need to also get some knowledge about the operation side of things of ML. I personally found Educative.io’s Grokking ML Interview course very helpful in that regard. It gets into the details about operational aspects of ML like how to choose metrics and online/offline model evaluation.
If you are relatively familiar with machine learning foundations and just need a brush up on it before the interview, I have used Springboard’s 51 Essential Machine Learning Interview Questions and Answers as a quick refresher.
Final Advice
Like I mentioned in my previous articles, one thing consulting firms REALLY care about is your ability to communicate, especially when it comes to complicated analytical concepts. Data scientist in consulting firms more than often need to work with clients who are NOT from an analytics background; so it’s essential that you showcase your ability to do just that. When explaining ML concepts and models, try to use as much plain English as possible instead of obscure jargons and make sure you can really explain them without getting into the details of the calculation; because you will encounter clients who ask you to “explain how clustering works without the math”.
Want to Read More About DS Interviews? Here Are Some Recommendations:
Concepts You Have to Know for Data Science Interviews — Part I: Distribution
Most frequently asked questions in data scientist interviewstowardsdatascience.com
Concepts You Have to Know for Data Science Interviews — Part II. Probability
Most frequently asked questions in data scientist interviewstowardsdatascience.com
Concepts You Have to Know for Data Science Interviews — Part III. Basic Supervised Learning Models
Most frequently asked questions in data scientist interviews for modelingtowardsdatascience.com
Concepts You Have to Know for Data Science Interviews — Part IV. Random Forest
Most frequently asked questions in data scientist interviewstowardsdatascience.com