Quantitative Analyst Intern applicants have rated the interview process at Wells Fargo with 3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 83% positive. To compare, the company-average is 65% positive. This is according to Glassdoor user ratings.
Candidates applying for Quantitative Analyst Intern roles take an average of 75 days to get hired, when considering 6 user submitted interviews for this role. To compare, the hiring process at Wells Fargo overall takes an average of 22 days.
Common stages of the interview process at Wells Fargo as a Quantitative Analyst Intern according to 6 Glassdoor interviews include:
Phone interview: 43%
Skills test: 29%
Presentation: 14%
One on one interview: 14%
Here are the most commonly searched roles for interview reports -
It was a 45 minute phone interview. They asked general questions related to data science, statistics and machine learning. The interviewer was very polite. I received the decision within a week.
Interview questions [1]
Question 1
General questions about machine learning and data science.
I applied online. I interviewed at Wells Fargo (Charlotte, NC) in Oct 2024
Interview
One round of video interview with both behavioral and easy technical questions. Then got invited to fly to Charlotte for a 2-day superday interview and a visit to their headquarters. Overall, a very pleasant journey and not hard interviews compared to other banks.
Interview questions [1]
Question 1
What is PCA? Tell me some Python packages you have used?...
First online hirevue for behavorial questions, then 1 hour phone interview, going through cv and technical, last two day case and business interview, one hour each. the cases will be sent 5 days prior, need study and slide for the presentation
I applied online. The process took 4 months. I interviewed at Wells Fargo (Charlotte, NC) in Sep 2023
Interview
It was 4 levels. First a phone interview where a few maths and machine learning questions were asked. Then a take home project to use machine learning models to predict loan approval. Then another interview to discuss my findings on the machine learning project. Finally another interview where machine learning questions were asked