Wells Fargo data scientist interviews vary by team. Candidates report resume deep-dives, ML and statistics fundamentals, take-home or case-study work, and behavioral questions on explaining results to non-technical stakeholders.
$142K
Avg. Base Comp
$166K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe Wells Fargo data science interviews in formats that differ a lot. Some report a single mixed round, others a take-home or case study, and one reports a longer sequence with an online assessment, several interviews and an HR round. Expect the process to depend on the business group.
The most repeated theme is your own resume. Candidates describe interviewers going closely through CV projects and internships, so be ready to explain your decisions and results. One reports basic Python questions, such as lists versus tuples, alongside deeper topics.
Technical depth appears in several forms. Candidates report statistics and debugging, the assumptions of linear regression, regression versus random forest, the full model lifecycle, and the math behind ML and NLP methods. One take-home asked for an auto loan decisioning model with a focus on fairness and explainability. The follow-up presentation reportedly focused more on reasoning than on accuracy, and that candidate also noted there was no coding round.
Communication is the other thread. Candidates were asked how they would explain complex data to non-technical stakeholders, or persuade management to fund a project and respond to pushback. One reports banking-specific questions on PSI and fraud-monitoring KPIs, along with SAS, so check the team's stack early.
Outcomes were mixed, with both offers and rejections reported. With a limited set of reports, treat this as a guide to themes rather than a fixed format.
Synthesized from 7 candidate reports by our editorial team.
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Real interview reports from people who went through the Wells Fargo process.
The part I underestimated was how much of the conversation came back to my own resume. The interview was a single round mixing behavioral and technical questions, and the interviewers went through my CV closely and asked me to go deeper on the projects I'd listed. Anything on the page was fair game, so I'd suggest walking through each project in detail, including the decisions you made and the results.
The technical questions centered on Python, with a few questions about other programming languages and my personal projects. Some were basic. I was asked what lists and tuples are, which sounds easy until you have to explain it live. The behavioral questions took up a real share of the time, so I'd prepare past-work stories as carefully as the code.
It was a difficult interview, and I did not receive an offer.
Prep tip from this candidate
Know every project on your CV well enough to explain your decisions and results, since this interview went line by line through the resume. Also be ready to explain Python basics like lists versus tuples, not just advanced topics.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Wells Fargo
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report different openings: an HR screen followed by a Python and SQL phone interview, an online assessment, or a resume screen mixing behavioral and technical questions. Which one you get may depend on the team.
Some candidates report a take-home, such as building a classification model or an auto loan decisioning model with fairness and explainability in mind. Another describes a technical case study with manager and director interviewers. Be ready to justify modeling choices and business framing.
Candidates report questions on statistics, debugging code aloud, linear regression assumptions, regression versus random forest, model lifecycle, and ML or NLP math. Python basics and SQL also came up. Depth likely varies by interviewer and team.
Several candidates report interviewers going through their CV closely and probing internships and projects. Typically this means explaining the decisions and results behind each item, mixed with behavioral questions about past work.
Candidates report questions about why they were interested in the program, explaining data to non-technical stakeholders, persuading management and handling pushback. One describes a five-person behavioral panel, and another a final HR conversation about fit and background.
