TD Bank Data Scientist interview typically runs 2 rounds: HR, hiring manager. It usually takes about 2 rounds over a few weeks and is fairly straightforward.
$126K
Avg. Base Comp
$139K
Avg. Total Comp
2
Typical Rounds
2-4 weeks
Process Length
We’ve seen TD Bank screen for data scientists who can stay grounded in business reality. Across candidate reports, the strongest signal is not exotic modeling knowledge but the ability to explain how you handled messy, end-to-end work: cleaning data, shaping features, and choosing methods that fit the problem. Multiple candidates mentioned being pressed on imbalanced datasets, with follow-up questions about why a technique was chosen and how it changed the outcome. That tells us the bar is less about naming the right algorithm and more about defending the tradeoffs behind it.
A recurring theme is that TD Bank seems to care a lot about whether your past projects translate cleanly into decision-making. Our candidates report detailed resume walkthroughs where interviewers dug into project specifics, then pushed on business framing and communication. Even when the questions were technical, they stayed practical — for example, comparing XGBoost and Random Forest or discussing recall versus AUC in the context of class imbalance. The non-obvious make-or-break here is clarity under scrutiny: can you connect a modeling choice to a business problem without drifting into theory for its own sake? That’s the pattern we’d prepare for most carefully.
Synthetized from 2 candidates reports by our editorial team.
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Real interview reports from people who went through the Td bank process.
From what I saw, the process was pretty straightforward and split into two parts. The first round was with HR and felt mostly like a fit check. They spent their time on the usual motivation questions — why I wanted the role, why TD Bank, and how I communicate — so it was less technical and more about whether I’d be a good match for the team and the company.
The second round was with the hiring manager, and that’s where things got much more detailed. They went through my resume carefully, especially my projects, and asked me to explain how I worked with data end to end. A lot of the discussion centered on how I cleaned and manipulated data, how I approached business problems, and how I dealt with imbalanced datasets. I was also asked what techniques I used in those situations, like resampling or class weighting, and how I chose evaluation metrics such as recall or AUC. The questions were practical rather than theoretical, but they still expected you to justify your choices clearly. I’d say the technical depth was moderate, with the main challenge being how well you could connect your past work to business impact. I didn’t get an offer in the end, but the process itself was clear and fairly standard.
Prep tip from this candidate
Be ready to walk through your projects in detail and explain exactly how you handled messy data and imbalanced classes. It would help to practice justifying why you chose resampling, class weighting, or recall/AUC for a given problem.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Td bank
Say you’re running an e-commerce website. You want to get rid of duplicate products that may be listed under different sellers, names, etc... in a very large database.
| Question | |
|---|---|
| Uber Eats Success | |
| Xgboost vs Random Forest | |
| Your Strengths and Weaknesses | |
| Interest Rates | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Subscription Overlap | |
| Monthly Customer Report | |
| Experiment Validity | |
| Find the Missing Number | |
| Cumulative Distribution | |
| Compute Deviation | |
| Bagging vs Boosting | |
| Prime to N | |
| String Shift | |
| 500 Cards | |
| Last Transaction | |
| Session Difference | |
| Maximum Profit | |
| Rain in N Days | |
| Button AB Test | |
| Alphabet Sum | |
| Paired Products |
Synthesized from candidate reports. Individual experiences may vary.
The first round is a fit check with HR. Candidates are typically asked motivation and communication questions, such as why they want the role, why TD Bank, and how they would work with others.
This round can include a coding portion with 2-3 LeetCode-style questions at an easy to medium level, followed by discussion of past data science projects. Interviewers may ask about topics like BFS, XGBoost vs. Random Forest, and how you approached data science work end to end.
The hiring manager goes deeper into the candidate's resume and project experience. Expect practical questions about cleaning and manipulating data, solving business problems, handling imbalanced datasets, and choosing techniques such as resampling, class weighting, and evaluation metrics like recall or AUC.
The second-round discussion includes additional technical and behavioral questions. This stage appears to focus on how well candidates justify their choices, connect their work to business impact, and demonstrate overall fit for the team.