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Visa Data Scientist Interview Questions and Answers
Visa Data Scientist Interview Experiences
4 interviews found
InfoOnly
I applied via Referral and was interviewed in May 2024. There was 1 interview round.
(2 Questions)
- Q1. Why are you interested in this role
- Q2. Provide an example how you solved a problem
- Ans.
I solved a problem by using machine learning algorithms to predict customer churn for a telecom company.
Identified relevant data sources such as customer demographics, usage patterns, and customer service interactions.
Preprocessed and cleaned the data to handle missing values and outliers.
Built and trained a machine learning model using algorithms like logistic regression and random forest.
Evaluated the model's perform...
Anonymous
(2 Questions)
- Q1. When is your Availability
- Q2. Expectation from us
- Ans.
I expect a supportive work environment, opportunities for growth, and clear communication.
Clear communication on project goals and expectations
Opportunities for professional development and growth
Supportive team environment
Regular feedback and performance evaluations
Interview Preparation Tips
Experience: The technical interview included a VP round which was more of a resume round. I concentrated my interview (VP round) on Machine learning which my field is. This was kind of a trick because many people screwed up this round. Hence, VP offered to me to work in VISA HQ.
Round: Technical Interview
Experience: This technical round included questions from almost all topics of CS (SQL query, coding, Networks, Security, Hadoop and Map-Reduce).
General Tips: Do’s :
1) Start right from August
2) Prepare basics of subjects
3) Try to code using pen and paper
4) Code as many problems as you can from all domains with only 1 language (C/C++/Java)
5) Keep a friend with you at all times during interviews. If you are selected for more than 1
companies in 1 slot, ask your friend to manage your interview schedules given he knows your
priorities. I was not in a position to attend more than 2 interviews in the same slot.
Don’ts :
1) Don’t lose hope if you are not shortlisted for your dream company or any company of your choice because being an IITian you have large number of better choices and which would fit in your profile better.
2) Don’t argue with the interviewers and never pretend that you are over smart in a given domain.
3) Do not take HR rounds lightly even if you are sitting for CS companies.
Final Tips :
Start your preparation early. Placement is 90% preparation and 10% luck.
College Name: IIT Kanpur
Interview Preparation Tips
Duration: 120 minutes
Total Questions: 42
Round: Test
Experience: There was a test for VISA which had following sections:
Assessment Composition:
1. Coding Skills: 2 Questions - Very simple problems - 30 marks total
2. Programming: 10 Questions - Simple Java Questions --Access modifiers, inline function, Red black tree, heap
3. Machine Learning Hadoop MCQ: 6 Questions You can't answer a single question if ML/BigData is remote-land for you.
4. Networking MCQ: 6 Questions - Again basics. Data layer questions. IP class basic question, fields in ethernet header.
5. Operating System: 6 Questions - Memory management mostly
6. Infrastructure: 6 Questions - Real life scenarios - server client questions.
7. Application Security: 6 Questions
Duration: 120 minutes
Total Questions: 42
Round: Interview
Experience: Questions from all CS subjects including coding
Tips: They shortlisted 29 students for the final interviews on Day 1 Slot 1.
There were 2 rounds of technical interview and 1 interview.
General Tips: Do’s
1) Start right from August
2) Prepare basics of subjects
3) Try to code using pen and paper
4) Code as many problems as you can from all domains with only 1 language (C/C++/Java)
5) Keep a friend with you at all times during interviews. If you are selected for more than 1 companies in 1 slot, ask your friend to manage your interview schedules given he knows your priorities. I was not in a position to attend more than 2 interviews in the same slot.
Don’ts
1) Don’t lose hope if you are not shortlisted for your dream company or any company of your choice because being an IITian you have large number of better choices and which would fit in your profile better.
2) Don’t argue with the interviewers and never pretend that your are over smart in a given domain.
3) Do not take HR rounds lightly even if you are sitting for CS companies.
Skills: C++
College Name: IIT Kanpur
Motivation: Role of my interest
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Selected
I applied via Referral and was interviewed in Sep 2024. There were 5 interview rounds.
1 python and 1 SQL coding along with ML questions mostly related to monitoring and basic ML
1 python code and also related to ML ...the recruiter came with a dataset to solve
Guestimates, project discussions and Case Studies
Project indepth discussion and case study related Fraud Detection
(1 Question)
- Q1. Aligning own interest with team requirement
(2 Questions)
- Q1. DBSCAN Clustering
- Q2. How does current work helps solving use cases for PayU
- Ans.
My current work involves analyzing transaction data to identify patterns and trends, which can help PayU optimize their payment processing services.
Analyzing transaction data to identify fraudulent activities and improve security measures for PayU
Developing predictive models to forecast transaction volumes and optimize payment processing times
Utilizing machine learning algorithms to personalize user experiences and inc...
Selected
I applied via campus placement at Lovely Professional University (LPU) and was interviewed before Jul 2023. There were 4 interview rounds.
Basic questions like stats, probability etc
Scenario based question
(1 Question)
- Q1. Questions on fundamentals
(1 Question)
- Q1. General questions
I applied via Campus Placement and was interviewed before Feb 2019. There were 3 interview rounds.
Interview Questionnaire
4 Questions
- Q1. 1. How to choose optimum probability threshold from ROC?
- Ans.
To choose optimum probability threshold from ROC, we need to balance between sensitivity and specificity.
Choose the threshold that maximizes the sum of sensitivity and specificity
Use Youden's J statistic to find the optimal threshold
Consider the cost of false positives and false negatives
Use cross-validation to evaluate the performance of different thresholds
- Q2. 2. How to test time series trend break up?
- Ans.
To test time series trend break up, statistical tests like Augmented Dickey-Fuller test can be used.
Augmented Dickey-Fuller test can be used to check if a time series is stationary or not.
If the time series is not stationary, we can use differencing to make it stationary.
After differencing, we can again perform the Augmented Dickey-Fuller test to check for stationarity.
If there is a significant change in the mean or va...
- Q3. 3. How do you deal with senior customer when you don't have enough data?
- Ans.
Communicate transparently and offer alternative solutions.
Explain the limitations of the available data and the potential risks of making decisions based on incomplete information.
Offer alternative solutions that can be implemented with the available data.
Collaborate with the customer to identify additional data sources or explore other options to gather more data.
Provide regular updates on the progress of data collect...
- Q4. 4. Do you prefer working in clear defined goal space or where customer isn't sure what he wants?
Interview Preparation Tips
Skills evaluated in this interview
(1 Question)
- Q1. SQL and background related questions
Akshay Agrawal
I applied via LinkedIn and was interviewed in Jun 2022. There were 2 interview rounds.
(3 Questions)
- Q1. What is rnn and lstm
- Ans.
RNN stands for Recurrent Neural Network and LSTM stands for Long Short-Term Memory. They are types of neural networks used for sequential data processing.
RNN is a type of neural network that can process sequential data by maintaining a memory of past inputs.
LSTM is a type of RNN that can handle the vanishing gradient problem and can remember long-term dependencies.
LSTM has gates that control the flow of information int...
- Q2. What is precision and recall
- Ans.
Precision and recall are two important metrics used to evaluate the performance of a classification model.
Precision measures the proportion of true positives among all the predicted positives.
Recall measures the proportion of true positives among all the actual positives.
Precision and recall are inversely related and a trade-off exists between them.
A high precision means that the model is good at predicting positive ca...
- Q3. What is regularisation
- Ans.
Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.
Regularisation helps to control the complexity of a model and reduce the impact of irrelevant features.
It adds a penalty term to the loss function, which encourages the model to have smaller weights.
There are different types of regularisation techniques such as L1 (Lasso) and L2 (Ridge) regularisa...
Interview Preparation Tips
- Deep Learning
- Machine Learning
- SQL
- Python
Skills evaluated in this interview
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Visa Data Scientist Interview Process
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