Recently Uber came to our campus for the recruitment of Data Scientist role. According to the recruitment process there were in total 5 rounds. Each round was an elimination round. The experience of each round is as follows:
Round 1 (Online Coding Assessment)
- Focused on Data Structure and Algorithm knowledge. I didn't remember the questions clearly, but the thing i know that there were in total 2 questions, in which the first one was Binary Search on Answers based and second one was based on Tree (Minimize Diameter of given tree by removing at most K nodes).
- Covered some MCQs also based on data manipulation, statistical analysis, and algorithmic thinking.
Round 2 (Technical Interview - Statistical Analysis and ML - 45 minutes)
- Evaluated statistical models, hypothesis testing, regression analysis, and data visualization. The interviewer asked some medium level questions of ML like ensemble learning, descent gradient, bias - variance trade off etc.
- Also Discussed machine learning algorithms (decision trees, random forests, gradient boosting).
- Analyzed a scenario (Case study) and proposed a suitable ML approach.
Round 3 (Technical Interview - Experimental Design and A/B Testing - 45 minutes)
- Assessed experimental design, metric selection, and A/B testing in the domain of ML and Statistics.
- Addressed challenges of how to conducting A/B tests at large scale.
- Overall this round well as I answered the questions and interviewer looked satisfied.
Round 4 (Hiring Manager Interview - Culture Fit and Goals - 30 minutes)
- In this round Interviewer asked question with respect to Uber’s values, mission, and career goals to test my alignment.
- At last he gave me opportunity to ask questions, If i have
In summary, the process covered technical depth and behavioral aspects, providing insights into Uber’s data-driven culture.