Visa Data Scientist interviews reported here span SQL and Python exercises, machine-learning cases, project discussion, behavioral evaluation, and—on one loop—ML system design and deployment.
$175K
Avg. Base Comp
$213K
Avg. Total Comp
4-6 rounds
Typical Rounds
3-5 weeks
Process Length
Visa Data Scientist candidates in these reports encountered a broad mix of applied analytics and machine-learning discussion. One candidate began with an online assessment containing SQL and Python, then described recruiter, technical, and behavioral stages. Another reported a first technical screen divided into SQL, Pandas data manipulation, and a team-relevant ML case on identifying high-intent users. That screen included discussion of classification with XGBoost, overfitting, and cross-validation.
A separate candidate described a hiring-manager conversation with a resume walkthrough and ML case study, followed by a loop that revisited the resume alongside ML system design, model deployment across the full lifecycle, behavioral questions, and a Python coding question. Their examples included feature engineering, monitoring, and retraining as part of designing a model end to end, plus a RAG-pipeline design prompt.
Prepare concise stories that connect prior ML work to the business need, then practice explaining design choices from data and features through evaluation, deployment, and monitoring. For hands-on work, focus on readable SQL—including aggregation and window-function concepts—and practical Python/Pandas manipulation. These accounts are limited in number, so the ordering and exact mix can vary.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Visa process.
There were 5 rounds + 1 recruiter round. 1st Round with HM(1 hr) - Resume walkthrough and ML case study Onsite loop
Round 1 - Again Resume walkthrough and ML System Design Round 2 - Resume walkthrough and Model Deployment(full lifecycle) Round 3 - Behavioral Round 4 - Resume walkthrough and Python coding question
Questions asked: It was mostly related to how would you create a model end to end - starting with feature engineering to model monitoring and retraining. Was asked to design a RAG pipeline Python medium level question in coding
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Topics based on recent interview experiences.
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| Question | |
|---|---|
| Closest SAT Scores | |
| Employee Salaries | |
| Top Three Salaries | |
| Alphabet Sum | |
| Encoding Categorical Features | |
| Total Spent on Products | |
| Sum to N | |
| Bagging vs Boosting | |
| Size of Joins | |
| Fewer Orders | |
| Resumable Fact Table Load | |
| Employees Before Managers | |
| Hurdles In Data Projects | |
| Production Model Monitoring | |
| RAG Strict Source Control | |
| Count Transactions | |
| Modifying a Billion Rows | |
| Filling Supermarket Bag | |
| Slow SQL Query | |
| Concurrent LLM Serving | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Swap Variables | |
| User Event Data Pipeline | |
| Mouse Search | |
| Delivery Assignments | |
| Location Feature Sharing | |
| Overfit Avoidance | |
| Solo ML Deployment | |
| Three Indexes Adding Zero |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported an online assessment with one SQL and one Python question before recruiter and later interviews. Another candidate’s first technical screen had SQL, Pandas, and an ML case study. Prepare for either entry point rather than assuming a single fixed opening stage.
Candidates reported intermediate SQL, including GROUP BY, average transaction amount, and LEAD/LAG concepts, plus Python tasks ranging from Fibonacci to Pandas-based grouping. Expect questions to emphasize clear implementation and data manipulation at the reported difficulty levels.
Candidates reported ML case studies tied to high-intent-user classification and prior projects. Discussion included XGBoost, preventing overfitting, and cross-validation; one hiring-manager interview also included an ML case study. Be ready to explain the business requirement as well as the modeling rationale.
One candidate reported a later loop with ML system design, full-lifecycle model deployment, behavioral questions, and Python coding. That report emphasized feature engineering, monitoring, retraining, and a RAG design prompt, so these topics may be relevant for teams assessing production ML work.
