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PivotRoots Data Scientist Interview Questions and Answers

Updated 22 Mar 2025

PivotRoots Data Scientist Interview Experiences

1 interview found

Interview experience
1
Bad
Difficulty level
Easy
Process Duration
2-4 weeks
Result
No response

I appeared for an interview in Feb 2025, where I was asked the following questions.

  • Q1. Tell me about yourself
  • Q2. What is a business problem that you tried to solve using data
  • Ans. 

    I analyzed customer churn data to identify key factors influencing retention and developed strategies to improve customer loyalty.

    • Conducted exploratory data analysis on customer behavior and churn rates.

    • Utilized logistic regression to identify significant predictors of churn.

    • Implemented a customer segmentation strategy based on usage patterns.

    • Developed targeted marketing campaigns to re-engage at-risk customers.

    • Monitor...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Worst company to ever

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When HR’s Chinese English made me drop the interview!
So, I talked to the HR yesterday about the interview. I asked Please send me the location But their English… bro I was shocked! It was like talking to someone jisne english nahi kuch ar hi seekh liya ho, if the HR’s English is this I can only imagine the rest of the company I decided to drop the interview with this chinese english😶‍🌫️
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Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before Aug 2023. There were 2 interview rounds.

Round 1 - One-on-one 

(2 Questions)

  • Q1. Difference between union and union all in SQL?
  • Q2. How to show top 5 in pandas?
  • Ans. 

    To show top 5 in pandas, use the nlargest() function.

    • Use the nlargest() function with the 'n' parameter set to 5 to get the top 5 values in a pandas DataFrame.

    • For example: df['column_name'].nlargest(5) will return the top 5 values in the specified column.

  • Answered by AI
Round 2 - Technical 

(1 Question)

  • Q1. If we have 3 numerical columns, which chart is a better representation these column?
  • Ans. 

    A scatter plot is a better representation for 3 numerical columns.

    • Use a scatter plot to show the relationship between the numerical columns.

    • Scatter plots are effective for visualizing correlations and patterns in data.

    • Each point on the plot represents a data point with values from all 3 columns.

  • Answered by AI

Interview Preparation Tips

Topics to prepare for Omnicom Media Group Data Analyst interview:
  • SQL Server
  • Pandas
  • Power Bi

Skills evaluated in this interview

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I applied via Campus Placement and was interviewed in Jul 2020. There were 5 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Questions from SQL, Python, Excel, Tableau and from my Projects

Interview Preparation Tips

Interview preparation tips for other job seekers - Be prepared with your projects properly

Data Scientist Interview Questions & Answers

FirstCry user image Swapnil Telrandhe

posted on 2 Mar 2021

I applied via LinkedIn and was interviewed in Sep 2020. There were 5 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Explain VIF
  • Ans. 

    VIF stands for Variance Inflation Factor, a measure of multicollinearity in regression analysis.

    • VIF is used to detect the presence of multicollinearity in regression analysis.

    • It measures how much the variance of the estimated regression coefficient is increased due to multicollinearity.

    • A VIF value of 1 indicates no multicollinearity, while a value greater than 1 suggests increasing levels of multicollinearity.

    • A commonl...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Focus on the projects that has been done in past

Interview Questionnaire 

1 Question

  • Q1. Role of data scientist

I applied via Recruitment Consulltant and was interviewed before Aug 2021. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Difference between CNN and MLP
  • Ans. 

    CNN is used for image recognition while MLP is used for general classification tasks.

    • CNN uses convolutional layers to extract features from images while MLP uses fully connected layers.

    • CNN is better suited for tasks that require spatial understanding like object detection while MLP is better for tabular data.

    • CNN has fewer parameters than MLP due to weight sharing in convolutional layers.

    • CNN can handle input of varying ...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Brush up basic statistics . Also prepare atleast 2 , 3 ML algorithms for the interview.

Skills evaluated in this interview

Be interview-ready. Browse the most asked HR questions.
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I applied via Walk-in and was interviewed in Mar 2020. There was 1 interview round.

Interview Questionnaire 

10 Questions

  • Q1. What is R square and how R square is different from Adjusted R square
  • Ans. 

    R square is a statistical measure that represents the proportion of the variance in the dependent variable explained by the independent variables.

    • R square is a value between 0 and 1, where 0 indicates that the independent variables do not explain any of the variance in the dependent variable, and 1 indicates that they explain all of it.

    • It is used to evaluate the goodness of fit of a regression model.

    • Adjusted R square t...

  • Answered by AI
  • Q2. Explain what do u understand by the team WOE and IV. What's the importance. Advantages and disadvantages
  • Ans. 

    WOE (Weight of Evidence) and IV (Information Value) are metrics used for feature selection and assessing predictive power in models.

    • WOE transforms categorical variables into continuous variables, making them more suitable for modeling.

    • IV quantifies the predictive power of a feature by measuring the separation between the good and bad outcomes.

    • For example, if a feature has an IV of 0.3, it indicates strong predictive po...

  • Answered by AI
  • Q3. What are variable reducing techniques
  • Ans. 

    Variable reducing techniques are methods used to identify and select the most relevant variables in a dataset.

    • Variable reducing techniques help in reducing the number of variables in a dataset.

    • These techniques aim to identify the most important variables that contribute significantly to the outcome.

    • Some common variable reducing techniques include feature selection, dimensionality reduction, and correlation analysis.

    • Fea...

  • Answered by AI
  • Q4. Which test is used in logistic regression to check the significance of the variable
  • Ans. 

    The Wald test is used in logistic regression to check the significance of the variable.

    • The Wald test calculates the ratio of the estimated coefficient to its standard error.

    • It follows a chi-square distribution with one degree of freedom.

    • A small p-value indicates that the variable is significant.

    • For example, in Python, the statsmodels library provides the Wald test in the summary of a logistic regression model.

  • Answered by AI
  • Q5. How to check multicollinearity in Logistic regression
  • Ans. 

    Multicollinearity in logistic regression can be checked using correlation matrix and variance inflation factor (VIF).

    • Calculate the correlation matrix of the independent variables and check for high correlation coefficients.

    • Calculate the VIF for each independent variable and check for values greater than 5 or 10.

    • Consider removing one of the highly correlated variables or variables with high VIF to address multicollinear...

  • Answered by AI
  • Q6. Difference between bagging and boosting
  • Ans. 

    Bagging and boosting are ensemble methods used in machine learning to improve model performance.

    • Bagging involves training multiple models on different subsets of the training data and then combining their predictions through averaging or voting.

    • Boosting involves iteratively training models on the same dataset, with each subsequent model focusing on the samples that were misclassified by the previous model.

    • Bagging reduc...

  • Answered by AI
  • Q7. Explain the logistics regression process
  • Ans. 

    Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.

    • It is a type of regression analysis used for predicting the outcome of a categorical dependent variable based on one or more predictor variables.

    • It uses a logistic function to model the probability of the dependent variable taking a particular value.

    • It is commo...

  • Answered by AI
  • Q8. Explain Gini coefficient
  • Ans. 

    Gini coefficient measures the inequality among values of a frequency distribution.

    • Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.

    • It is commonly used to measure income inequality in a population.

    • A Gini coefficient of 0.4 or higher is considered to be a high level of inequality.

    • Gini coefficient can be calculated using the Lorenz curve, which plots the cumulati...

  • Answered by AI
  • Q9. Difference between chair and cart
  • Ans. 

    A chair is a piece of furniture used for sitting, while a cart is a vehicle used for transporting goods.

    • A chair typically has a backrest and armrests, while a cart does not.

    • A chair is designed for one person to sit on, while a cart can carry multiple items or people.

    • A chair is usually stationary, while a cart is mobile and can be pushed or pulled.

    • A chair is commonly found in homes, offices, and public spaces, while a c...

  • Answered by AI
  • Q10. How to check outliers in a variable, what treatment should you use to remove such outliers
  • Ans. 

    Outliers can be detected using statistical methods like box plots, z-score, and IQR. Treatment can be removal or transformation.

    • Use box plots to visualize outliers

    • Calculate z-score and remove data points with z-score greater than 3

    • Calculate IQR and remove data points outside 1.5*IQR

    • Transform data using log or square root to reduce the impact of outliers

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Explain the concept properly, if not able to explain properly then take a pause and try again with some examples. Be confident.

Skills evaluated in this interview

Are these interview questions helpful?

Interview Questionnaire 

1 Question

  • Q1. What PCA, Decision tree and computer vision
  • Ans. 

    PCA is a dimensionality reduction technique, decision tree is a classification algorithm, and computer vision is a field of study focused on enabling computers to interpret and understand visual information.

    • PCA is used to reduce the number of variables in a dataset while retaining the most important information.

    • Decision trees are used to classify data based on a set of rules and conditions.

    • Computer vision involves usin...

  • Answered by AI

Skills evaluated in this interview

Interview Questionnaire 

1 Question

  • Q1. Diffe heilman algorithm
  • Ans. 

    Diffie-Hellman algorithm is a key exchange protocol used to securely exchange cryptographic keys over a public channel.

    • It is based on the concept of discrete logarithm problem.

    • It involves two parties, Alice and Bob, who generate their own private and public keys.

    • The public keys are exchanged and used to generate a shared secret key.

    • The shared secret key is used for encryption and decryption of messages.

    • It is widely use...

  • Answered by AI

Skills evaluated in this interview

I applied via Company Website and was interviewed in Sep 2020. There were 4 interview rounds.

Interview Questionnaire 

3 Questions

  • Q1. Linear regression , Linear regression assumption , market mix model , SQL joins
  • Q2. Puzzle, guess estimate
  • Q3. Project related questions

Interview Preparation Tips

Interview preparation tips for other job seekers - Focus on basic Ml algorithm
Linear regression
Logistics regression
Difference between bagging and boosting

PivotRoots Interview FAQs

How to prepare for PivotRoots Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at PivotRoots. The most common topics and skills that interviewers at PivotRoots expect are Machine Learning, Python, SQL, Bigquery and Snowflake.
What are the top questions asked in PivotRoots Data Scientist interview?

Some of the top questions asked at the PivotRoots Data Scientist interview -

  1. What is a business problem that you tried to solve using d...read more
  2. Tell me about yours...read more

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Overall Interview Experience Rating

1/5

based on 1 interview experience

Difficulty level

Easy 100%

Duration

2-4 weeks 100%
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PivotRoots Data Scientist Salary
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₹12.6 L/yr - ₹22.5 L/yr
9% more than the average Data Scientist Salary in India
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