JP Morgan Interview Experience

One of the nation’s oldest banking organizations, JPMorgan Chase (NYSE: JPM), has been around for more than 200 years. A part of the Dow Jones Industrial Average is JP Morgan shares. They are present in more than 100 markets and have over 250,000 workers. These JP Morgan statistics scream data. With more than 30,000 databases, 3.5 billion user accounts, and more than 150 petabytes of data, JPMorgan Chase is unquestionably a force to be reckoned with in the financial industry. JP Morgan is the best business, in my opinion, if you want to work as a data scientist in the Fintech industry.

Interview Process:

A coding screen on Collabedit is the first step in the interview process. A site visit follows this. The team is necessary for the onsite. The onsite will focus heavily on those areas if the team is UX and analyst-heavy. On one end of the technical spectrum, there are certain teams conducting rigorous ML and data science, and on the other, there are roles that focus heavily on analysts. The great thing about JP Morgan is that you can find a job there even if, as a data scientist, you strongly lean one way or the other. Some teams do occasionally assign take-home tasks, which reflects the ethos of the group.

Questions asked during the interview:

1. How would you describe linear regression to a non-technical person?
2. How do ridge regression and lasso regression vary from one another?
3. How would you describe map-reduce programming to a developer?
4. What safeguards exist against overfitting?
5. Explain the Naive Bayes algorithm.
6. Why does logistic regression need the application of feature scaling?
7. Provide a case study illustrating how you used linear regression.
8. What testing techniques do you employ?
9. Determine where two arrays of integers intersect.
10. Discover the nth prime number.
11. To determine where in an array of integers a particular value is located, use a binary search.
12. What steps should you take to fix data biases?

Reflecting on the Questions:

JP Morgan is heavily investing in its technology and workforce in order to advance data science. The mix of coding, machine learning, and analysis questions in their interviews is good. Due to the maturity of the data science team, there is a clear division of labor between data engineers, analysts, and scientists. The interview reflects the fact that the data scientist’s responsibilities are particular to the industry and the product. You can most certainly get a job with one of the oldest financial organizations in the world if you have strong analytical and data sense as well as in-depth product knowledge.