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McKesson Data Scientist Interview Questions and Answers
McKesson Data Scientist Interview Experiences
1 interview found
Anonymous
I applied via Job Portal
(4 Questions)
- Q1. What is probability
- Ans.
Probability is a measure of the likelihood of an event occurring.
Probability ranges from 0 (impossible) to 1 (certain)
It can be calculated by dividing the number of favorable outcomes by the total number of outcomes
Used in various fields like statistics, gambling, and risk assessment
- Q2. Difference between classification and regression
- Ans.
Classification is for predicting discrete labels, while regression is for predicting continuous values.
Classification predicts categories or labels, such as spam or not spam.
Regression predicts continuous values, such as house prices or temperature.
Classification uses algorithms like logistic regression, decision trees, and support vector machines.
Regression uses algorithms like linear regression, polynomial regression
- Q3. Family background
- Q4. Difference between smart work and hard work
Interview Preparation Tips
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
I applied via Recruitment Consulltant and was interviewed in Dec 2024. There were 3 interview rounds.
The assignment involved analyzing sample data from Inshorts to extract valuable insights.
(2 Questions)
- Q1. Can you elaborate on the projects that you have worked on?
- Ans.
I have worked on projects involving predictive modeling, natural language processing, and machine learning algorithms.
Developed a predictive model to forecast customer churn for a telecommunications company
Implemented sentiment analysis using natural language processing techniques on social media data
Utilized machine learning algorithms to classify fraudulent transactions for a financial institution
- Q2. What were the key discussion points regarding the assignment?
- Ans.
Key discussion points regarding the assignment
The methodology used to analyze the data
The key findings and insights derived from the analysis
Any challenges faced during the assignment and how they were overcome
Recommendations for future improvements or further analysis
(1 Question)
- Q1. Statistics, ML models, Assignment, Case Study
I applied via Referral and was interviewed in Jun 2024. There were 2 interview rounds.
The assessment consists of a dataset for which we are required to build a machine learning model and submit the results along with code and detailed documentation
(3 Questions)
- Q1. What are ensemble models
- Ans.
Ensemble models are machine learning models that combine multiple individual models to improve predictive performance.
Ensemble models work by aggregating predictions from multiple models to make a final prediction.
Common types of ensemble models include Random Forest, Gradient Boosting, and AdaBoost.
Ensemble models are often more accurate and robust than individual models.
They can reduce overfitting and increase genera...
- Q2. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model c...
- Q3. A lot of tree based questions
Skills evaluated in this interview
Gourav Sharma
Selected
I applied via Approached by Company and was interviewed before Jun 2022. There were 8 interview rounds.
(1 Question)
- Q1. 1st round was more of Mathematical foundation screening before selecting for 2nd round
Assignment was similar to work you gonna do as a Data Scientist at Inshorts.
(1 Question)
- Q1. Discussion on assignment
(1 Question)
- Q1. Data Science related
(1 Question)
- Q1. Advanced data science related questions
(1 Question)
- Q1. HR team on basic HR questions
(1 Question)
- Q1. With Co-founder to introduce and check you
I applied via Naukri.com and was interviewed in Sep 2020. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. Explain in detail your favourite algorithm or the algorithm you used in your project
Interview Preparation Tips
I applied via Recruitment Consulltant and was interviewed in Dec 2024. There were 3 interview rounds.
The assignment involved analyzing sample data from Inshorts to extract valuable insights.
(2 Questions)
- Q1. Can you elaborate on the projects that you have worked on?
- Ans.
I have worked on projects involving predictive modeling, natural language processing, and machine learning algorithms.
Developed a predictive model to forecast customer churn for a telecommunications company
Implemented sentiment analysis using natural language processing techniques on social media data
Utilized machine learning algorithms to classify fraudulent transactions for a financial institution
- Q2. What were the key discussion points regarding the assignment?
- Ans.
Key discussion points regarding the assignment
The methodology used to analyze the data
The key findings and insights derived from the analysis
Any challenges faced during the assignment and how they were overcome
Recommendations for future improvements or further analysis
(1 Question)
- Q1. Statistics, ML models, Assignment, Case Study
I applied via Referral and was interviewed in Jun 2024. There were 2 interview rounds.
The assessment consists of a dataset for which we are required to build a machine learning model and submit the results along with code and detailed documentation
(3 Questions)
- Q1. What are ensemble models
- Ans.
Ensemble models are machine learning models that combine multiple individual models to improve predictive performance.
Ensemble models work by aggregating predictions from multiple models to make a final prediction.
Common types of ensemble models include Random Forest, Gradient Boosting, and AdaBoost.
Ensemble models are often more accurate and robust than individual models.
They can reduce overfitting and increase genera...
- Q2. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model c...
- Q3. A lot of tree based questions
Skills evaluated in this interview
Selected
I applied via Approached by Company and was interviewed before Jun 2022. There were 8 interview rounds.
(1 Question)
- Q1. 1st round was more of Mathematical foundation screening before selecting for 2nd round
Assignment was similar to work you gonna do as a Data Scientist at Inshorts.
(1 Question)
- Q1. Discussion on assignment
(1 Question)
- Q1. Data Science related
(1 Question)
- Q1. Advanced data science related questions
(1 Question)
- Q1. HR team on basic HR questions
(1 Question)
- Q1. With Co-founder to introduce and check you
I applied via Naukri.com and was interviewed in Sep 2020. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. Explain in detail your favourite algorithm or the algorithm you used in your project
Interview Preparation Tips
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McKesson Data Scientist Interview Process
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