What candidates report for Wells Fargo Quantitative Analyst interviews: behavioral screens, PDE, regression and Monte Carlo questions, and a case study and presentation on the rotational program track.
$158K
Avg. Base Comp
$180K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Wells Fargo quantitative interviews are reported to be more mathematical than the bank's general analyst loops, but the accounts are few and the formats differ, so treat what follows as two candidate reports rather than a fixed template.
One Quantitative Analyst candidate from late 2023 opened with behavioral questions, including a difficult academic situation they had overcome and how they would adjust a presentation they had given. The conversation then turned technical: PDEs, linear regression and Monte Carlo simulation, including how to calculate and interpret standard error. The questions tested whether the candidate could actually work through the math and reasoning rather than simply explain a concept. That candidate did not receive an offer.
The second account, from the Risk Analytics Decision Science track of the Quantitative Analyst Rotational Program, looked different. It began with a behavioral screen that ended with a few classical ML questions, such as why logistic regression is considered linear. Next came a 75-minute Hackerrank with multiple-choice items on which R code produced a given output, then a 72-hour take-home credit decisioning case study. The process finished with two days in Charlotte presenting that case study, with questions on feature selection and class imbalance.
Be ready to work through the math, not just recall it. Practice PDEs, regression and Monte Carlo simulation, including standard error. Rehearse a project or model you can explain end to end, covering the choices you made and how you would describe them to a non-technical audience.
Synthesized from 20 candidate reports by our editorial team.
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Real interview reports from people who went through the Wells Fargo process.
No behavioral questions. I was given a list of 5 questions to solve after the HackerRank assessment. The questions included one on PDEs, one on Monte Carlo simulation, one on portfolio volatility, and two very easy Python and C++ domain knowledge questions.
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Synthesized from candidate reports. Individual experiences may vary.
Reports open with a conversation about your background. One Quantitative Analyst candidate began with behavioral questions, such as a difficult academic situation and how to adjust a presentation, before turning technical. The rotational program candidate heard scripted behavioral questions followed by a few classical ML questions, including why logistic regression is considered linear.
One Quantitative Analyst candidate reported PDEs, linear regression and Monte Carlo simulation, with standard error to calculate and interpret. That candidate felt the bar was working through the math rather than describing concepts.
The Risk Analytics Decision Science candidate took a 75-minute Hackerrank covering programming, math and statistics, and data analysis; the programming items were multiple choice on which R code produced a given output. A 72-hour take-home followed: build a joint auto loan credit decisioning model, write a report and prepare a presentation. The other account did not mention this stage.
The rotational program candidate spent two days in Charlotte. Day one was an hour of behavioral questions on explaining complex ideas to non-technical stakeholders and handling team conflict. Day two was an hour presenting the case study, with questions on feature selection and class imbalance. The other Quantitative Analyst account did not describe a final round.
