Me postulé en línea. El proceso tomó 2 meses. Acudí a una entrevista en C3 AI (Londres, Inglaterra) en nov 2024
Entrevista
Got an OA with 8 pretty standard data science questions and 1 coding question, these questions can be found in previous reviews. I was invited to a three-round interview session after that:
• Problem Solving (45 minutes): Standard data science question regarding experimentation setup and ML system design.
• Machine Learning (45 minutes): Asked questions about my project background and design choices, very curious into understanding why certain choices were made so be prepared to explain yourself.
• Coding (1 hour): Starts with a general discussion about coding experience and principles. Then you're given a simple coding question, find palindromes, and after which a slightly harder NumPy-related question. Didn't know there was going to be two coding problems to solve, so I wouldn't have spent as much time being verbose on the first one. Ask your recruiter if there will be 1 or 2 coding problems so that you can plan accordingly.
Didn't hear back until I reached out a week later and they were still gathering feedback. A day later I got an automatic rejection letter.
Preguntas de entrevista [1]
Pregunta 1
Coding problems:
1. Implement a function is_palindrome that checks whether a given string s is a palindrome. A palindrome is a word, phrase, or sequence that reads the same forwards and backwards, ignoring spaces, punctuation, and case differences. Your function should return True if s is a palindrome and False otherwise.
2. You are given a dataset represented by a 2D NumPy array, where each row is a sample, and each column is a feature. Your task is to implement a StandardScaler class, which will standardize the data by removing the mean and scaling to unit variance for each feature.
You need to implement the following methods:
fit: Given a 2D NumPy array, this method calculates and stores the mean and standard deviation for each column (feature). This method does not return anything.
transform: Given a 2D NumPy array, this method returns a standardized array where each feature has zero mean and unit variance, using the mean and standard deviation stored by the fit method. If fit has not been called, transform should raise an exception.
Me postulé en línea. Acudí a una entrevista en C3 AI (Singapur)
Entrevista
Hackerrank --> three tech interviews (proceed to the next one if you pass the current one) each round is 1 hour long --> hiring manager interview (1 hour)--> VP interview.
Preguntas de entrevista [1]
Pregunta 1
tech interviews: 1) (1 hour) traditional ML based case study, 2) (1 hour) ML concept deep dive, and 3) (1 hour) coding (leet-code medium)
Resume screening -> technical assessment -> 4 rounds of interviews:
- personal projects, simple questions not there to trick you
- situational questions: "what would you do if..."
- machine learning: starts from the very basics (stats and probabilities) to more up to date models
- coding: medium leet code
Me postulé en línea. El proceso tomó 3 semanas. Acudí a una entrevista en C3 AI (Londres, Inglaterra) en oct 2025
Entrevista
I applied directly after seeing a job advert on LinkedIn. There are MCQ and coding assessment on Hackerank, followed by a screening interview. It all went well and got invited to the technical day.
To prepare for the technical interview, I went through all materials and questions shared by others on this website and once I was half way, I noticed that the questions tend to be similar, except the pairwise coding. I recommend you go through questions here to be better prepared for the technical day.
The interview was generally okay and the team was nice. Started off with Case Study (30 mins); followed by ML questions (30 mins); and finally coding (1 hour). There is barely time in-between to switch so expect to transition very quickly. For the case study, think out loud it helped me to figure the actual problem, as they only share the problem and you figure the rest out.
The coding was fair, I had done a couple of Leetcode but they started off with Linear regression etc, kinda caught me off guard and wasted 35 mins on it. Though the program ran, the interviewer said there isn't enough time to complete second question, and we shared our coding experiences and clarity on a few questions. I am pretty confident in stats and ML knowledge but the issue could have been coding; so make sure you are up to speed with anything that can be thrown at you.
Two days later I received a rejection email. No reason after having spend so much time is a bit disrespectful but we move on.
Preguntas de entrevista [1]
Pregunta 1
Case study: Waste reduction in chain stores. They simply stated that and I described it as a demand forecasting problem that can be solved with Linear Regression. Besides clarification questions, It was fine and they took it.
MLQ
1. Difference between Supervised and Unsupervised Learning, and give examples
2. Difference between bagging and boosting;
3. Bias and variance, and explain in the context of Bagging/boosting
4. Performance metrics; what does AUC mean, interpret AUC of 50%
5. Gradient descent
6. Overfitting and Underfitting and how to overcome them in Decision Trees
Coding: Implement linear regression, numpy, and plotting importance scores
