This time, I'm sharing a review of a Programhelp student's interview for a data science position at Visa. The entire process, from the phone interview to the onsite interview, was highly technical, especially with SQL, ML theory, and Python data processing being tested in almost every round. Open-ended questions also featured a significant portion, highlighting both on-the-spot communication and structured thinking.
Delivery and Preparation
This student submitted her resume directly from the official website without OA, and the process quickly progressed to a phone call from HR and then to an interview. She has internship experience in data analysis, and her resume focuses on SQL and Pandas practical projects, and she applied for the Data Scientist position.
Programhelp helped her with structured simulations a week before her interview, including shorthand for high-frequency concepts in SQL, Pandas operational remediation, and strategies for common open-ended questions in the e-interviews (especially how to hold the field steady and advance ideas slowly).
Electric surface process (two rounds)
Round 1: SQL Theory + Practice
This round is centered around SQL, with the first half being conceptual questions:
- RANK() vs ROW_NUMBER() exclusionary rule
- HAVING and WHERE Scenarios of application
She was a little nervous about HAVING and WHERE, so the Programhelp voice prompted her, "Remember to emphasize that HAVING is for filtering the aggregated results." She immediately added: "HAVING is used to filter the aggregate result after group by, for example, we want to filter the customers whose total number of orders is more than 10."
The second half is a practical SQL query: "Find bank card numbers with more than 10 transactions in the past 30 days."
Although the logic is not complicated, she forgot to write at first GROUP BY card_idThe interviewer was very impressed with the clarity of her explanations, and the fact that she was reminded by our voice assistants. The interviewer valued the clarity of her explanatory thoughts, and the on-site communication felt good.
Round 2: Open-ended case studies
The question is: "How do you determine the richest country in the last 10 years?"
This question is actually quite open-ended, and many people will fall into the dilemma of "speaking too superficially or too broadly". This student said "GDP" directly at the beginning, and the interviewer asked, "Is it total GDP or per capita?" She hesitated for two seconds. She hesitated for two seconds, and then the voice prompt came: "Quickly ask a rhetorical question to define the dimensions, and then expand the scenario."
She immediately added: "I understand that we can start by defining multiple wealth dimensions, such as total GDP, GDP per capita, and gearing ratio. Different definitions may lead to different conclusions, and we can do comparative analysis."
At this time, the whole rhythm is stabilized. She then talked about how to do time series backtracking based on the transaction amount + time, and mentioned the points of attention of data cleaning and currency normalization, and the interviewer was very satisfied with the overall situation.
Onsite (4 rounds)
Round 1: Deep Dive Project + Data Quality Issue Analysis
The project part asked her about previous predictive models she had done, and she mentioned a churn prediction case. the interviewer immediately followed up with, "How did you find that the model performed poorly with certain user groups?"
She mentioned that it was through segment-level evaluation that she found the AUC to be significantly low, and further found that the training and test data time spans were inconsistent, resulting in a drifting data distribution.Programhelp prepared her with a framework for answering similar questions in advance, so she gave her solution on the spot, including:
- Adjust sampling time period
- Calibrate drift using online evaluation
- Constructing rolling training set with time windows
Round 2: Machine Learning Fundamentals
This round is a pure ML theory quiz:
- Difference between GBM vs Random Forest
- How to handle label imbalance
- Precision vs Recall trade-off
- Meaning of AUC = 0.5
She was very solid in this round, especially in her answer to Precision/Recall where she gave an example of fraud detection, and the interviewer commented that she "not only knows the theory but can map it to real use cases". The interviewer commented that she "not only knows the theory but can map it to real use cases".
Round 3: Communication Skills + Program Matching
There were no technical questions, and she mainly talked about her experience with the infrastructure team. The interviewer cared more about the communication style, problem decomposition ability, and the ability to collaborate on the implementation rather than just talking about the model. In this round, she mentioned the experience of using airflow to build an automated pipeline and collaborating with the backend to trigger the model to go live, which is a plus.
Round 4: Python Programming + Pandas Practice
She knew she would be tested on Pandas before the round began, and we made a point of reviewing the SQL-to-Pandas conversion routines for her.
The interviewer asked first:
- Scenarios for Multi-Threading vs Multi-Processing?
- The design core of OOP?
- Why does Generator save memory?
The next practical question is: Use Pandas to implement conditional filtering after groupby aggregation, which is equivalent to SQL's GROUP BY HAVING.
She initially wrote .groupby().filter(lambda x: …) but made a mistake in the lambda logic. A voice prompt reminded her that len(x) and x.sum() are not the same thing. She corrected the problem promptly, and the code ran successfully. The interviewer even asked her to optimize it to a chained expression.
Frequently Asked Questions
Q1: Is Visa's DS more analytical or algorithmic? It's generally analytically oriented, but the technical requirements are quite high. A solid foundation in SQL, data structures, and machine learning is required.
Q2: How long should I prepare for Onsite? It is recommended to prepare systematically at least a week in advance, especially ML theory questions + SQL-to-Pandas conversion questions are very high frequency.
Q3: How do I answer an open-ended question? First define the indicator, then explain the framework, and finally talk about the hypothesis analysis. Don't just "answer the conclusion".
Q4: What will be asked in a resume project? Ask from background, goals, modeling, deployment all the way to the end, and be prepared for technical details and business motivation at every step.
The base does not determine the ceiling
Visa's DS interview is not fancy, but it is "standard + solid". If you do not have a strong foundation in SQL, are not familiar with Pandas, and are not familiar with ML theory, then you will basically be overturned in one round.
Programhelp Assisting this student in steadying the pace during the interview process, the voice assist played a key role in SQL logic, open-ended question indicator The voice helper played a key role in SQL logic, open question metrics, and Pandas chaining operations. It's not about doing the questions instead of you, but about pulling you back when your thoughts break down.
If you are also ready to rush to Visa, Stripe, Capital One and other DS positions, welcome to come to us for a systematic combing, we will help you to turn "know how to do the questions" into "be able to pass".
