Me postulé en línea. El proceso tomó 4 semanas. Acudí a una entrevista en C3 AI
Entrevista
First contact was with recruiter email. The interview with one of existing employee (not hiring manager) to discuss position, my experience,etc. second part of interview was a 5 page white paper on use cases for AI given my industry knowledge (prefer not to specify). You are given 7 calendar days and a few links for guidance. I submitted it and never heard back. I followed up with the recruiter three times and finally received a “come back to us in a few months” email. No feedback or any other type of communication. I felt the purpose of the job postings was to get prospects to write white papers which can then be actually used by employees as an idea generator. For someone who spent many hours on it and be sent a generic email is really disappointing. If I had gotten feedback or a 30 minute convo with hiring manager, my feelings would’ve been different. Honestly very disappointed in how they treat people. Do not spend time with white paper unless you actually talk to hiring manager and believe it’s a real job posting.
Preguntas de entrevista [1]
Pregunta 1
General experience with analytics and data science
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
