Candidates describe a TD Bank data scientist process with an early screen, a technical or coding stage, and a resume-heavy conversation. Several stress explaining work to non-technical stakeholders.
$105K
Avg. Base Comp
$125K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Candidate reports for the TD Bank Data Scientist role vary in length, from two rounds to a four-round onsite, so confirm the schedule with your recruiter. What recurs across reports is a heavy focus on how you talk about your own work. One candidate describes a dedicated experience-focused technical interview, and another says the hiring manager went through the resume closely, including how they cleaned data, handled imbalanced classes and chose metrics such as recall or AUC.
Communication with non-technical stakeholders comes up repeatedly. Candidates report being asked how they explain results to non-technical clients, how they handle disagreement, and how they work in cross-functional teams. One was also asked about risk modeling or fraud detection and the bias/variance tradeoff.
Coding is reported at a modest level. One candidate describes two or three easy-to-medium LeetCode questions, including breadth-first search, and another mentions about 20 minutes of Python and SQL. A candidate who received an offer describes live SQL with joins, CTEs and aggregates, a case study with revenue calculations, and a 15-minute self-chosen deck. Be ready to compare XGBoost and Random Forest, rehearse project walkthroughs with business impact, and practice short SQL. The reports are few and vary by team, so treat them as a guide rather than a fixed format.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Td bank process.
1 round (3 more to go), seemed like a good start and in depth screen. some basic scanning of my resume and had to answer simple behavioral questions. very clear outline of the process and stuff like that. I had to describe what I would do if I sent out a mistake
Questions asked: Explain a time you had to explain technical methods to a non-technical audience. Explain risk modeling or fraud detection practices. Bias/variance tradeoff.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Td bank
Explain the difference between XGBoost and random forest and give an example where you would use one over the other
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Synthesized from candidate reports. Individual experiences may vary.
One candidate describes a first round with HR that felt like a fit check, covering why the role, why TD Bank and how they communicate. Another describes an in-depth early screen with a basic resume scan and simple behavioral questions. Expect to explain your motivation clearly and discuss communication style.
Candidates report coding in different forms. One describes two or three easy-to-medium LeetCode questions, including breadth-first search. Another mentions about 20 minutes of Python and SQL within a longer round. Practice basic graph traversal, Python and SQL, since the format may differ by team.
Candidates report detailed discussion of past data science projects, with one describing a hiring manager conversation on data cleaning, imbalanced datasets, resampling or class weighting, and recall or AUC. Model questions such as XGBoost versus Random Forest were also reported. You should be able to justify each choice and tie it to business impact.
Several candidates report behavioral questions about explaining technical methods to non-technical audiences, handling disagreement, working with other teams, and what they would do after sending out a mistake. Prepare specific examples with a clear situation, your action and the result.
One candidate who received an offer describes a four-round onsite: behavioral, a case study with revenue calculations, a 15-minute deck on any topic, and live SQL with three questions covering joins, CTEs and aggregates. Other candidates did not report these stages, so they may apply only to some teams or formats.
