Santander data scientist interviews, as three candidates describe them, mix manager conversations with technical questions on linear models, NLP and transformers, SQL, and banking compliance. Experiences ranged from an offer to early rejection.
$118K
Avg. Base Comp
$140K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Three candidates describe Santander data scientist interviews, and the accounts differ, so prepare for more than one format. One candidate for a Senior Associate role in Credit and Lending had a hiring manager conversation, then a 45-50 minute technical discussion. It began with BERT and attention, then moved to interpretable linear and logistic regression: what each term in the logistic equation means, how L1 and L2 regularization work, and how to optimize them. The candidate also got a question on null value handling, covering business input, mean/median/mode imputation and regression-based imputation. They said weak answers on regression interpretability likely cost them a third-round virtual onsite.
A second candidate, who received an offer, described a first round that combined background questions, practical machine learning, SQL and statistics. They were also asked about NLP and neural networks, including how tokenization works in transformers, and about how models comply with banking regulation. They found the difficulty suited to a mid-level data scientist and said banking experience was valued but not required up front. They also reported long gaps between responses and a role that was put on hold, closed and reopened.
The third candidate had an HR screen and a manager interview, and was not invited to technical rounds. They felt the conversation centered on their reasons for leaving their current job and their background. Be ready to explain your motivation and career move clearly and calmly.
The practical takeaway: know classical, explainable models as well as modern NLP, be able to discuss how you would keep models compliant with banking guidelines, and practice clear SQL.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Santander process.
I had a positive experience with the interview team and HR folks at Santander — they were genuinely approachable and the conversations felt natural. That said, the process itself was quite disorganized. There were long stretches between responses, and at one point the role got put on hold entirely, then closed and reopened from scratch, which added a lot of uncertainty to the timeline.
The first round covered screening and technical ground. They asked about my professional background and then moved into practical machine learning and SQL questions. I remember a technical screening that included SQL work, which felt reasonable in scope but required solid query optimization thinking. The discussion also touched on regulatory metrics and standards — they were interested in understanding how I'd ensure models comply with banking guidelines, which made sense given Santander's fintech focus.
The technical questions themselves weren't too steep for someone with a few years of data science experience. We covered statistics basics, natural language processing concepts, and neural networks. One specific question I got was about how tokenization works in transformers, which tests whether you understand the mechanics of modern NLP pipelines rather than just knowing they exist. The overall difficulty felt calibrated to mid-level experience — nothing exotic, but you needed to have done the work. Interestingly, while they clearly valued banking domain knowledge, they weren't strict about requiring it upfront, which was refreshing.
Prep tip from this candidate
Be ready to explain regulatory compliance and model validation in a banking context — that came up repeatedly. Also, brush up on transformer internals like tokenization and attention mechanisms, not just high-level NLP concepts. Given the process delays, clarify timeline expectations early and follow up proactively if you don't hear back within their stated window.
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Featured question at Santander
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Synthesized from candidate reports. Individual experiences may vary.
One candidate reports an HR screening call that was straightforward, and another describes a first round that began with professional background questions. Expect to explain your experience and why you want the role; timing and format may vary by team.
Candidates report a manager round focused on resume, background and the team's work. In one case it stayed mostly non-technical, and in another the candidate felt the questions centered on their reasons for leaving their current role and ended without technical rounds.
Candidates report technical questions on BERT and attention, tokenization in transformers, statistics, neural networks and SQL. One 45-50 minute discussion focused on logistic regression terms, L1 and L2 regularization, optimization and null imputation. Questions on how models comply with banking guidelines also came up.
One candidate mentions a third-round two-hour virtual onsite they were not moved forward to after a technical discussion. Another reports an offer but also long delays and a role placed on hold, then reopened, so applicants may want to follow up proactively.
