I applied through a recruiter. The process took 4 months. I interviewed at Bloomberg
Interview
- 1st round - HR round questions about data structures and their use cases
-2nd round - Python programming around data structures, problems involving data basic data structures(lists, dictionaries etc) , Basic ML and stats(measures of central tendency etc),error calculations for ml
-3rd round- Pandas questions, Was given a Bloomberg dataset.
-Final round - Senior Manager, Motivation based questions mainly.
Interview questions [1]
Question 1
1st round - Interviewed last year and had a leetcode style questions, this time around however the questions were Bloomberg and role specific eg from a list remove invalid words(think of it as a data cleaning but with basic data structures) .
2nd round - Best advice is practice using kaggle on as many datasets as possible, stratascratch is okay but found kaggle more useful.
Consists of 3 parts. The first is a leetcode-easy python coding problem. The next is a real-world solution based on statistical knowledge. The last is a taxonomy problem that requires you to find out flaws in the original one.
I applied through a recruiter. I interviewed at Bloomberg (New York, NY)
Interview
I was contacted by a recruiter for the role. After an initial HR screening, there were four rounds of interviews. The first round was with the hiring manager/team lead and focused on my previous experience and general questions related to the role. The second round was a technical interview with two team members and included a live Python coding exercise. The third round was a take-home Excel assessment with basic questions related to financial statements. The final round was on-site with two team leads and included a mix of behavioral questions and discussion about past experience.
For the Python round, focus on solving LeetCode easy-level questions and reviewing basic Data Modeling, ML and Stats.
Overall, it was a positive experience and HR was quick with updates throughout the process.
The first round was behavioral, followed by a technical interview focused on data modeling concepts and data quality considerations rather than coding. The second technical round involved exploratory data analysis using Python and pandas.
Interview questions [1]
Question 1
Find any issues in this dataset (picture of a dataset).