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Clarivate Data Scientist Interview Questions and Answers

Updated 23 Oct 2024

Clarivate Data Scientist Interview Experiences

2 interviews found

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

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

Round 1 - Coding Test 

Graph based question, acyclic graph

Round 2 - Technical 

(4 Questions)

  • Q1. Probability question
  • Q2. ML design interview
  • Q3. Algorithm coding
  • Q4. NLP based design question

I applied via Approached by Company and was interviewed in Feb 2022. There were 2 interview rounds.

Round 1 - HR 

(5 Questions)

  • Q1. What are your salary expectations?
  • Q2. What is your family background?
  • Q3. Where do you see yourself in 5 years?
  • Q4. What are your strengths and weaknesses?
  • Q5. Tell me about yourself.
Round 2 - Assignment 

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare for the Critical Behavioral Questions

Data Scientist Interview Questions Asked at Other Companies

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Q5. coding question of finding index of 2 nos. having total equal to ... read more

Interview questions from similar companies

Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(1 Question)

  • Q1. Tell me about your self
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Asked about some machine learning concepts like NLP, TensorFlow etc
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
Selected Selected

I applied via Referral and was interviewed before May 2023. There were 4 interview rounds.

Round 1 - Aptitude Test 

180 mins of online test with camera ON. Major topics include Excel, Aptitude, Python, Statistics and Case Study

Round 2 - Technical 

(2 Questions)

  • Q1. Explain Apriori Method
  • Ans. 

    Apriori method is a popular algorithm for frequent itemset mining in data mining.

    • Used for finding frequent itemsets in transactional databases

    • Based on the concept of association rule mining

    • Involves generating candidate itemsets and pruning based on support threshold

    • Example: If {milk, bread} is a frequent itemset, then {milk} and {bread} are also frequent

  • Answered by AI
  • Q2. Explain train-test in Scikit learn
  • Ans. 

    Train-test split is a method used to divide a dataset into training and testing sets for model evaluation in Scikit learn.

    • Split the dataset into two subsets: training set and testing set

    • Training set is used to train the model, while testing set is used to evaluate the model's performance

    • Common split ratios are 70-30 or 80-20 for training and testing sets

    • Example: X_train, X_test, y_train, y_test = train_test_split(X, y,

  • Answered by AI
Round 3 - One-on-one 

(2 Questions)

  • Q1. Explain about projects in current company
  • Q2. Why do you want to move to an individual contributor role from a managerial position
Round 4 - One-on-one 

(2 Questions)

  • Q1. Why do you want to join Wolters Kluwer?
  • Q2. Discussion around analytics and managerment reporting deliverables in current org.

Interview Preparation Tips

Topics to prepare for Wolters Kluwer Data Scientist interview:
  • Advanced Excel
  • Python
  • Power Bi
  • Pivot Table
Interview preparation tips for other job seekers - You must be an advanced Excel user and decent knowledge of Python.

Skills evaluated in this interview

Interview Questionnaire 

1 Question

  • Q1. Please tell me something about yourself.What is your experience? What are your goals and ambitions?Why We should hire you? Strengths and weaknesses etc.

I applied via Approached by Company and was interviewed before Sep 2021. There were 3 interview rounds.

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 - Aptitude Test 

Explain dynamic programming with memoization

Round 3 - HR 

(2 Questions)

  • Q1. Where are you from, and why are you joining the company
  • Q2. Why are you joining the company

Interview Preparation Tips

Interview preparation tips for other job seekers - First, they will ask about the breadth of your ML skills and the depth going forward
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 before Apr 2023. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. Identify geometric alorithm pattern
  • Ans. 

    Geometric algorithm patterns involve solving problems related to geometric shapes and structures.

    • Identifying and solving problems related to points, lines, angles, and shapes

    • Utilizing geometric formulas and theorems to find solutions

    • Examples include calculating area, perimeter, angles, and distances in geometric figures

  • Answered by AI
  • Q2. If minimal data, which would you train for categorical prediction model?
  • Ans. 

    I would train a decision tree model as it can handle categorical data well with minimal data.

    • Decision tree models are suitable for categorical prediction with minimal data

    • They can handle both numerical and categorical data

    • Decision trees are easy to interpret and visualize

    • Examples: predicting customer churn, classifying spam emails

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Study fundamentals of ml algorithms and how the process geometrically

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
-

I was interviewed before Mar 2023.

Round 1 - Technical 

(1 Question)

  • Q1. What's the difference between k means and knn
  • Ans. 

    K-means is a clustering algorithm while KNN is a classification algorithm.

    • K-means is unsupervised learning, KNN is supervised learning

    • K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors

    • K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)

    • Example: K-means can be used to group customers based on purc...

  • Answered by AI

Skills evaluated in this interview

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

(1 Question)

  • Q1. Asked from resume about RAG

Interview Preparation Tips

Interview preparation tips for other job seekers - Asked everything from my resume

Clarivate Interview FAQs

How many rounds are there in Clarivate Data Scientist interview?
Clarivate interview process usually has 2 rounds. The most common rounds in the Clarivate interview process are HR, Assignment and Coding Test.
How to prepare for Clarivate 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 Clarivate. The most common topics and skills that interviewers at Clarivate expect are Analytical, Data Analysis, Python, Automation and Business Intelligence.
What are the top questions asked in Clarivate Data Scientist interview?

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

  1. NLP based design quest...read more
  2. Probability quest...read more
  3. ML design interv...read more

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Clarivate Data Scientist Interview Process

based on 1 interview

Interview experience

4
  
Good
View more
Clarivate Data Scientist Salary
based on 12 salaries
₹7.2 L/yr - ₹25 L/yr
24% more than the average Data Scientist Salary in India
View more details

Clarivate Data Scientist Reviews and Ratings

based on 3 reviews

2.2/5

Rating in categories

3.4

Skill development

2.9

Work-life balance

3.4

Salary

1.4

Job security

1.4

Company culture

1.4

Promotions

1.4

Work satisfaction

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