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Genpact Data Scientist and Machine Learning Engineer Interview Questions and Answers

Updated 25 Jul 2024

Genpact Data Scientist and Machine Learning Engineer Interview Experiences

1 interview found

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

Python arrays loops data structures

Round 2 - Technical 

(2 Questions)

  • Q1. Project discussion technical challenges
  • Q2. Deep learning neural network

Interview questions from similar companies

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. What is your favourite algorithm and how have you implemented it?
  • Ans. 

    My favorite algorithm is Random Forest, which I have implemented for predicting customer churn in a telecom company.

    • Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.

    • I have implemented Random Forest in Python using scikit-learn library for a telecom company to predict customer churn based on various features like call d...

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
-

I applied via Company Website and was interviewed in Mar 2023. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - One-on-one 

(1 Question)

  • Q1. Basic ML questions and previous project-related queries
Round 3 - One-on-one 

(1 Question)

  • Q1. Advanced ML questions
Round 4 - HR 

(1 Question)

  • Q1. Project handle queires

Interview Preparation Tips

Topics to prepare for TCS Data Scientist and Machine Learning Engineer interview:
  • Data Science
Interview preparation tips for other job seekers - explain point to point and keep it simple. learn all machine learning algorithms and deep learning concepts and statistics concepts
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
-
Result
No response

I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.

Round 1 - Technical 

(6 Questions)

  • Q1. Which GenAI projects I have worked on
  • Q2. What is the context window in LLMs
  • Ans. 

    Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.

    • Context window helps LLMs capture dependencies between words in a sentence.

    • A larger context window allows the model to consider more context but may lead to increased computational complexity.

    • For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo

  • Answered by AI
  • Q3. What is top_k parameter
  • Ans. 

    top_k parameter is used to specify the number of top elements to be returned in a result set.

    • top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.

    • For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.

    • In natural language processing tasks, top_k can be used to limit the number of possible next words in a

  • Answered by AI
  • Q4. What are regex patterns in python
  • Ans. 

    Regex patterns in Python are sequences of characters that define a search pattern.

    • Regex patterns are used for pattern matching and searching in strings.

    • They are created using the 're' module in Python.

    • Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.

  • Answered by AI
  • Q5. What are iterators and tuples
  • Ans. 

    Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.

    • Iterators are used to loop through elements in a collection, like lists or dictionaries

    • Tuples are similar to lists but are immutable, meaning their elements cannot be changed

    • Example of iterator: for item in list: print(item)

    • Example of tuple: my_tuple = (1, 2, 3)

  • Answered by AI
  • Q6. Do I have REST API experience
  • Ans. 

    Yes, I have experience working with REST APIs in various projects.

    • Developed RESTful APIs using Python Flask framework

    • Consumed REST APIs in data analysis projects using requests library

    • Used Postman for testing and debugging REST APIs

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Case Study 

Forecasting problem - Predict daily sku level sales

Round 2 - Technical 

(2 Questions)

  • Q1. What is difference between bias and variance
  • Ans. 

    Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.

    • Bias is the error introduced by approximating a real-world problem, leading to underfitting.

    • Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

    • High bias can cause a model to miss relevant relationships between features and target variable.

    • High variance can cause a model to ...

  • Answered by AI
  • Q2. Parametric vs non parametruc model
  • Ans. 

    Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.

    • Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

    • Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

    • Examples of parametric models inc...

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(3 Questions)

  • Q1. About Machine learning basics, activation functions linear regression, cnn, all basics..
  • Q2. About project questions, about sdlc basic 3 questions
  • Q3. About Why not used another model for training?

Interview Preparation Tips

Interview preparation tips for other job seekers - prepare Machine learning basics and project details well..
Interview experience
1
Bad
Difficulty level
Moderate
Process Duration
-
Result
No response

I was interviewed in May 2024.

Round 1 - One-on-one 

(2 Questions)

  • Q1. Tell me about your self?
  • Q2. What is maths and stats
  • Ans. 

    Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.

    • Maths involves the study of numbers, quantities, shapes, and patterns.

    • Stats involves collecting, analyzing, interpreting, and presenting data.

    • Maths is used to solve equations, calculate probabilities, and model real-world phenomena.

    • Stats is used to make informed decisions, draw conclusions, and test hypotheses.

    • Both ma...

  • Answered by AI
Round 2 - Coding Test 

Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

Interview Preparation Tips

Interview preparation tips for other job seekers - Be very serious on every answer
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Number of Duplicate words in a string
  • Ans. 

    Count the number of duplicate words in a string.

    • Split the string into words using a delimiter like space or punctuation.

    • Create a dictionary to store the count of each word.

    • Iterate through the words and increment the count in the dictionary.

    • Count the number of words with count greater than 1 as duplicates.

  • Answered by AI
  • Q2. Chunking in LLM
  • Ans. 

    Chunking in LLM refers to breaking down text into smaller chunks for better processing by the language model.

    • Chunking helps improve the efficiency of the language model by breaking down large text inputs into smaller segments.

    • It can help the model better understand the context and relationships within the text.

    • Chunking is commonly used in natural language processing tasks such as text summarization and sentiment analys

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Don’t add your photo or details such as gender, age, and address in your resume. These details do not add any value.
View all tips
Round 2 - Coding Test 

Basic DP, Array Questions

Round 3 - One-on-one 

(1 Question)

  • Q1. Resume Walkthrough and Discussion, Medium level coding questions
Round 4 - One-on-one 

(1 Question)

  • Q1. Discussion with Manager
Round 5 - HR 

(1 Question)

  • Q1. Normal HR round
Interview experience
1
Bad
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

I applied via Naukri.com and was interviewed in Nov 2022. There were 3 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Properly align and format text in your resume. A recruiter will have to spend more time reading poorly aligned text, leading to high chances of rejection.
View all tips
Round 2 - Technical 

(2 Questions)

  • Q1. How tfidf works in NLP
  • Ans. 

    TF-IDF is a statistical measure used to evaluate the importance of a word in a document.

    • TF-IDF stands for Term Frequency-Inverse Document Frequency

    • It is used to weigh a word's importance in a document by considering its frequency in the document and across all documents

    • The formula for TF-IDF is: TF-IDF = TF * IDF

    • TF (Term Frequency) measures how frequently a term appears in a document

    • IDF (Inverse Document Frequency) mea...

  • Answered by AI
  • Q2. What is the difference between group by and window function in sql
  • Ans. 

    Group by is used to group data based on a column while window function is used to perform calculations on a specific window of data.

    • Group by is used to aggregate data based on a specific column

    • Window function is used to perform calculations on a specific window of data

    • Group by is used with aggregate functions like sum, count, avg, etc.

    • Window function is used with analytical functions like rank, lead, lag, etc.

    • Group by ...

  • Answered by AI
Round 3 - Technical 

(2 Questions)

  • Q1. Tell me about the project you worked on 4 years back
  • Ans. 

    Developed a predictive model to forecast customer churn for a telecom company.

    • Used machine learning algorithms like logistic regression and random forest.

    • Preprocessed and cleaned the dataset by handling missing values and outliers.

    • Performed feature engineering to create new variables for better model performance.

    • Evaluated model performance using metrics like accuracy, precision, and recall.

    • Implemented the model in prod

  • Answered by AI
  • Q2. Why are you leaving your current company
  • Ans. 

    Seeking new challenges and opportunities for growth.

    • Looking for a more challenging role that aligns with my career goals.

    • Seeking a company that values innovation and encourages professional development.

    • Want to work in a more collaborative and diverse team environment.

    • Desire to explore new technologies and industries.

    • Current company lacks opportunities for advancement or career growth.

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - They took my resume from Naukri and contacted me, and 1st round of interview was scheduled. Questions were mostly based on projects in the resume, basics of machine learning, statistics, and hypothesis testing.
The recruiter said that you are selected for 2nd round and this will be with the Vice president.
That vice president did not have any time sense and the interview was scheduled at 10:30 in the night and he joined at 10:45, he started asking about the projects which was done 4 years ago(which was not stall mentioned in the resume). Later started asking about the current project.

Then he asked how did you do hyperparameter tuning, I said using GridSearchCV, he asked did you not use scikit-learn?. He does not even know that GridSerachCv is one of the class inside scikit-learn.

Later I contacted recruiter, she started ignoring my calls, after trying for 5 times, I asked did you get any feedback,? she said still I have not got. How much time they want to say if I am selected for the next round or rejected? I said even if I am not selected, please inform me. But still she has not said anything.

Skills evaluated in this interview

Genpact Interview FAQs

How many rounds are there in Genpact Data Scientist and Machine Learning Engineer interview?
Genpact interview process usually has 2 rounds. The most common rounds in the Genpact interview process are Coding Test and Technical.
What are the top questions asked in Genpact Data Scientist and Machine Learning Engineer interview?

Some of the top questions asked at the Genpact Data Scientist and Machine Learning Engineer interview -

  1. Project discussion technical challen...read more
  2. Deep learning neural netw...read more

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Genpact Data Scientist and Machine Learning Engineer Interview Process

based on 1 interview

Interview experience

5
  
Excellent
View more

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