Upload Button Icon Add office photos

Filter interviews by

KCS GROUP Data Scientist Interview Questions and Answers

Updated 28 May 2021

KCS GROUP Data Scientist Interview Experiences

1 interview found

I applied via Naukri.com and was interviewed in Nov 2020. There were 4 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. They asked about projects mentioned in resume. How we done our projects. What's concept in projects like how we handle imbalence data, what is cnn, what is lstm . They asked machine learning, deep learning...

Interview Preparation Tips

Interview preparation tips for other job seekers - Main thing attend all scheduled interview is key concept to clear interview

Interview questions from similar companies

Interview experience
2
Poor
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
No response

I applied via Referral and was interviewed in Nov 2024. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. SQL and pandas coding
  • Q2. Resume projects deep dive

Interview Preparation Tips

Interview preparation tips for other job seekers - No matter what kinds of questions indicated in HR email, be prepared for behavioral questions all the time
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Aug 2024. There were 2 interview rounds.

Round 1 - Coding Test 

*****, arjumpudi satyanarayana

Round 2 - Technical 

(5 Questions)

  • Q1. What is the python language
  • Ans. 

    Python is a high-level programming language known for its simplicity and readability.

    • Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.

    • It emphasizes code readability and uses indentation for block delimiters.

    • Python has a large standard library and a vibrant community of developers.

    • Example: print('Hello, World!')

    • Example: import pandas as pd

  • Answered by AI
  • Q2. What is the code problems
  • Ans. 

    Code problems refer to issues or errors in the code that need to be identified and fixed.

    • Code problems can include syntax errors, logical errors, or performance issues.

    • Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.

    • Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.

  • Answered by AI
  • Q3. What is the python code
  • Ans. 

    Python code is a programming language used for data analysis, machine learning, and scientific computing.

    • Python code is written in a text editor or an integrated development environment (IDE)

    • Python code is executed using a Python interpreter

    • Python code can be used for data manipulation, visualization, and modeling

  • Answered by AI
  • Q4. What is the project
  • Ans. 

    The project is a machine learning model to predict customer churn for a telecommunications company.

    • Developing predictive models using machine learning algorithms

    • Analyzing customer data to identify patterns and trends

    • Evaluating model performance and making recommendations for reducing customer churn

  • Answered by AI
  • Q5. What is the lnderssip
  • Ans. 

    The question seems to be incomplete or misspelled.

    • It is possible that the interviewer made a mistake while asking the question.

    • Ask for clarification or context to provide a relevant answer.

  • Answered by AI

Interview Preparation Tips

Topics to prepare for IBM Data Scientist interview:
  • Python
  • Machine Learning
Interview preparation tips for other job seekers - No

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Jul 2024. There were 2 interview rounds.

Round 1 - One-on-one 

(3 Questions)

  • Q1. Tell me about yourself?
  • Ans. 

    I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.

    • Background in statistics and machine learning

    • Experience in solving complex problems using data-driven approaches

    • Passionate about leveraging data to drive insights and decision-making

  • Answered by AI
  • Q2. Describe in detail about one of my main project.
  • Ans. 

    Developed a predictive model for customer churn in a telecom company.

    • Collected and cleaned customer data including usage patterns and demographics.

    • Used machine learning algorithms such as logistic regression and random forest to build the model.

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

    • Implemented the model into the company's CRM system for real-time predictions.

  • Answered by AI
  • Q3. Few questions related to my projects.
Round 2 - Technical 

(1 Question)

  • Q1. Questions on Basics python(Since i am fresher)

Interview Preparation Tips

Interview preparation tips for other job seekers - Overall, it was a good experience for me. Very friendly interviewers. I couldn't make it after the second round. I came to know where I was lacking.
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
3
Average
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
No response

I applied via Naukri.com and was interviewed in Mar 2024. There were 3 interview rounds.

Round 1 - One-on-one 

(3 Questions)

  • Q1. Machine learning algorithms.
  • Ans. 

    Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.

    • Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

    • Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

    • These algorithms require training data to learn patte...

  • Answered by AI
  • Q2. Credit risk life cycle
  • Q3. Pandas related questions
Round 2 - One-on-one 

(3 Questions)

  • Q1. Steps of developing a credit risk model
  • Ans. 

    Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.

    • 1. Define the problem and objectives of the credit risk model.

    • 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

    • 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

    • 4. Select a suitable machine learning algorithm such as logi...

  • Answered by AI
  • Q2. Pandas related questions
  • Q3. Bagging and Boosting
Round 3 - One-on-one 

(3 Questions)

  • Q1. Explain AIC and BIC
  • Ans. 

    AIC and BIC are statistical measures used for model selection in the context of regression analysis.

    • AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

    • BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...

  • Answered by AI
  • Q2. Difference between xgboost and lightgbm
  • Ans. 

    XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.

    • XGBoost is known for its accuracy and performance on structured/tabular data.

    • LightGBM is faster and more memory-efficient, making it suitable for large datasets.

    • LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.

  • Answered by AI
  • Q3. Bagging and boosting

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-

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

Round 1 - Technical 

(1 Question)

  • Q1. Pandas basics and SQL joins nlp

Interview Preparation Tips

Interview preparation tips for other job seekers - It was good
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
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Aptitude Test 

Very easy and but selection is basis

Round 2 - Coding Test 

We are looking to hire incredible Python Developers interested in working with a US Startup. If you are truly passionate about designing and building machine learning solutions using python, you’re looking for a job where you can work from anywhere- and we mean anywhere and are excited about gaining experience in a Startup, then this is the position for you. Be it your next vacation spot or a farm out in the country, if you have working internet, you can work remotely from your chosen location. No long commutes or rushing to in-person meetings. Ready to work hard and play harder? Let’s work together.

Interview Preparation Tips

Interview preparation tips for other job seekers - Should have:
• Excellent understanding of machine learning techniques and algorithms, such as Neural
Network, Random Forest, Gradient Boosting
• Experience with common data science toolkits, such as Anaconda, Python, SQL
• Strong data visualization skills and ability to present insights from analysis.
• Proficient in writing queries using SQL.
• Good programming skills in Python, with an ability to write production ready code.
Nice to have:
• Experience with working with version control software like Git etc.
• Experience with AWS Stack, Apache Spark is preferred.
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(4 Questions)

  • Q1. What is stemming and lematization
  • Ans. 

    Stemming and lemmatization are techniques used in natural language processing to reduce words to their base or root form.

    • Stemming is a process of reducing words to their base form by removing suffixes.

    • Lemmatization is a process of reducing words to their base form by considering the context and part of speech.

    • Stemming is faster but may not always produce a valid word, while lemmatization is slower but produces valid wo...

  • Answered by AI
  • Q2. Python coding about default values in class and funtions
  • Q3. Kmeans clustering evaluation
  • Q4. How to measure multicollinearity
  • Ans. 

    Multicollinearity can be measured using correlation matrix, variance inflation factor (VIF), or eigenvalues.

    • Calculate the correlation matrix to identify highly correlated variables.

    • Use the variance inflation factor (VIF) to quantify the extent of multicollinearity.

    • Check for high eigenvalues in the correlation matrix, indicating multicollinearity.

    • Consider using dimensionality reduction techniques like principal componen

  • Answered by AI

Tell us how to improve this page.

People are getting interviews through

based on 1 KCS GROUP interview
Job Portal
100%
Low Confidence
?
Low Confidence means the data is based on a small number of responses received from the candidates.
Software Developer
7 salaries
unlock blur

₹1.5 L/yr - ₹6.5 L/yr

Software Engineer
4 salaries
unlock blur

₹5.5 L/yr - ₹6.5 L/yr

Quality Analyst
4 salaries
unlock blur

₹4.2 L/yr - ₹5.1 L/yr

HR Executive
4 salaries
unlock blur

₹2.5 L/yr - ₹3 L/yr

Accounts Manager
4 salaries
unlock blur

₹6.6 L/yr - ₹7.2 L/yr

Explore more salaries
Compare KCS GROUP with

Tata Group

4.2
Compare

Reliance Industries

4.0
Compare

Aditya Birla Group

4.1
Compare

Mahindra & Mahindra

4.1
Compare

Calculate your in-hand salary

Confused about how your in-hand salary is calculated? Enter your annual salary (CTC) and get your in-hand salary
Did you find this page helpful?
Yes No
write
Share an Interview