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SPRINKLR Data Scientist Interview Questions, Process, and Tips

Updated 30 Sep 2024

Top SPRINKLR Data Scientist Interview Questions and Answers

View all 7 questions

SPRINKLR Data Scientist Interview Experiences

4 interviews found

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
Not Selected
Round 1 - Coding Test 

3 question were asked in 90 min time

Round 2 - Technical 

(2 Questions)

  • Q1. What is precison ?
  • Ans. 

    Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

    • Precision is calculated as TP / (TP + FP), where TP is true positives and FP is false positives.

    • It measures the accuracy of positive predictions made by the model.

    • A high precision indicates that the model is good at predicting positive cases without many false positives.

    • For example, in a binary classificatio...

  • Answered by AI
  • Q2. What is large lang. model ?
  • Ans. 

    A large language model is a type of artificial intelligence model that is capable of understanding and generating human language at a large scale.

    • Large language models use deep learning techniques to process and generate text.

    • Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Hard
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Campus Placement and was interviewed before Jul 2023. There were 3 interview rounds.

Round 1 - Coding Test 

Mostly data related question and two coding questions which we had to do in python only

Round 2 - One-on-one 

(3 Questions)

  • Q1. They asked the mathematics behind Adam Optimizer
  • Q2. What is attention in term of data science?
  • Ans. 

    Attention in data science refers to the mechanism that allows models to focus on specific parts of the input data.

    • Attention mechanisms help models to weigh the importance of different input features.

    • They are commonly used in natural language processing tasks such as machine translation and text summarization.

    • Attention can improve the performance of models by allowing them to selectively focus on relevant information.

    • Ex...

  • Answered by AI
  • Q3. Questions on whether you would use precision and recall in real life example for measuring accuracy.
Round 3 - One-on-one 

(2 Questions)

  • Q1. How would you judge the efficiency of LLMs
  • Ans. 

    Efficiency of LLMs can be judged based on various factors such as accuracy, speed, resource consumption, and interpretability.

    • Evaluate accuracy by comparing LLM predictions with ground truth labels

    • Assess speed by measuring the time taken for LLM to process data

    • Analyze resource consumption in terms of memory and computational power usage

    • Consider interpretability by examining how easily LLM decisions can be understood

    • Use...

  • Answered by AI
  • Q2. Questions on CV mostly

Skills evaluated in this interview

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Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed before Jul 2023. There were 2 interview rounds.

Round 1 - Coding Test 

Hacker Rank Test on coding questions

Round 2 - Technical 

(2 Questions)

  • Q1. Whats difference between KNN and Kmeans
  • Ans. 

    KNN is a supervised learning algorithm used for classification and regression, while Kmeans is an unsupervised clustering algorithm.

    • KNN is a supervised learning algorithm that classifies a new data point based on the majority class of its k-nearest neighbors.

    • Kmeans is an unsupervised clustering algorithm that partitions data into k clusters based on similarity.

    • KNN requires labeled training data, while Kmeans does not r...

  • Answered by AI
  • Q2. Coding a training pipeline
  • Ans. 

    Coding a training pipeline involves creating a process to train machine learning models efficiently.

    • Define the data preprocessing steps

    • Split the data into training and validation sets

    • Choose a machine learning algorithm to train the model

    • Tune hyperparameters to optimize model performance

    • Evaluate the model using metrics like accuracy or loss

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare for fundamental Machine learning and Other Data Science concepts,

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed before Oct 2022. 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 Resume tips
Round 2 - Coding Test 

Basic leet code questions easy to a bit of medium

Round 3 - Technical 

(3 Questions)

  • Q1. Explain your projects from resume
  • Q2. Cross questions on the terms in resume
  • Q3. How do you handle class imbalance
  • Ans. 

    Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.

    • Use resampling techniques like oversampling the minority class or undersampling the majority class.

    • Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

    • Adjust class weights in the model to give more importance to the minority class.

  • Answered by AI
Round 4 - HR 

(1 Question)

  • Q1. Basic HR round was there

Skills evaluated in this interview

Interview questions from similar companies

Data Scientist Interview Questions & Answers

LTIMindtree user image Abhishek Srivastav

posted on 16 Mar 2015

Interview Questionnaire 

3 Questions

  • Q1. Code For parse Traingle
  • Ans. 

    Code for parsing a triangle

    • Use a loop to iterate through each line of the triangle

    • Split each line into an array of numbers

    • Store the parsed numbers in a 2D array or a list of lists

  • Answered by AI
  • Q2. Asci value along with alphabets(both capital and small)
  • Ans. 

    The ASCII value is a numerical representation of a character. It includes both capital and small alphabets.

    • ASCII values range from 65 to 90 for capital letters A to Z.

    • ASCII values range from 97 to 122 for small letters a to z.

    • For example, the ASCII value of 'A' is 65 and the ASCII value of 'a' is 97.

  • Answered by AI
  • Q3. Would you like to go for Hire studies

Interview Preparation Tips

Round: Test
Experience: First round was through Elitmus.
If you want to be in IT industry must appear it atleast once, for core also u can try it.
It's usually a tough exam but if u are good in maths , apti you will crack it.
Tips: Focus more on Reasoning part. this is the most difficult part.
practise paragraphs reading and solving(Average level)(Infosys level or less)
If you need any kind of help you can contact me via email or can even ring me.
I would recomend everybody to appear this exam with minimum of one month dedicated preparation
Duration: 120 minutes
Total Questions: 60

Round: Coding Round on their own plateform
Experience: It was little difficult to write codes on some other plateform. But time was enough to cope up.
Tips: Try writing as many programs you can write in C, C++ and JAVA not on paper, on compiler . while giving this exam you can select any of these three languages. Based on that your technical Interview will be taken.

Round: Technical Interview
Experience: Its easy one if you have hands on on programing
Tips: Explore and explore .

Round: HR Interview
Experience: Most difficult round for me(I feel myself a little weak in English). But stay calm. And be cheerful.
I still don't know the exact answer of the question but conversation gone for about 20 minutes on this topic.
He din't seem satisfied with me. Btw most of the people says to say no. You can take your call according to the situation.
Tips: Stay calm. Have as much Knowledge about the organisation. Try to make your Intro as much interesting as possible with achivements, hobbies etc. Ya English plays most important role here.

General Tips: Always have faith in yourself. And remember Everything happens for some good reason
Skill Tips: Dont go deep in OS, DBMS but have rough idea about all the topics
Skills: C, C++, DATA STRUCTURE, DBMS, OS
College Name: GANDHI INSTITUTE OF ENGINEERING AND TECHNOLOGY
Motivation: I wanted a job. :)
Funny Moments: A number of stories are there related to this job.
One is I already had an offer so I booked my ticket to home from Bangalore But at very last moment my father told me that you should never miss any chance, go for it. I went and interview date was postponded due to some reasons. I got a mail at 10:30 pm saying I have to attend interview next day morning at 8:30 pm. I ran to get printout of that mail. The venue was 3 hour journey from my place so I din't sleep for the whole night because i knew that if I ll sleep, I would not be able to wake up But I din't studied also because it would have lead to sleep. And Without having sleep and last moment study I made it.

Skills evaluated in this interview

Interview Questionnaire 

1 Question

  • Q1. First Round: Basic Statistics, Basic Python Programming, Past Projects. Second Round: Past Projects, Questions from computer vision, NLP, SQL, Basic Python.
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(2 Questions)

  • Q1. Discussion about my resume
  • Q2. General questions
Round 2 - Coding Test 

Coding round basic packages , and basic python coding

Interview experience
1
Bad
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. What kind of project you worked
  • Ans. 

    I have worked on projects involving predictive modeling, natural language processing, and computer vision.

    • Predictive modeling: Developed machine learning models to predict customer churn for a telecom company.

    • Natural language processing: Built a sentiment analysis tool to analyze customer reviews for a retail company.

    • Computer vision: Implemented a facial recognition system for access control in a secure facility.

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Interviewer not in mood of listen. When he asked question he didn't even wait for answer and ask another question. Some kind of confused person I guess.

I applied via Recruitment Consultant and was interviewed in Mar 2021. There was 1 interview round.

Interview Questionnaire 

1 Question

  • Q1. Explain about your projects

Interview Preparation Tips

Interview preparation tips for other job seekers - Interviewer was looking for Data science experience in infrastructure that is building a solution for remedy ticket
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Aug 2023. There were 3 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 Resume tips
Round 2 - Technical 

(1 Question)

  • Q1. All about my previous projects and questions based on that. Also, they checked the knowledge of Generative AI & LLM.
Round 3 - One-on-one 

(1 Question)

  • Q1. All about my previous projects and questions based on that. It was a techno-managerial round which was also focused on situation based scenarios. Taken by Head of AI/ML - CoE.
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SPRINKLR Interview FAQs

How many rounds are there in SPRINKLR Data Scientist interview?
SPRINKLR interview process usually has 2-3 rounds. The most common rounds in the SPRINKLR interview process are Coding Test, Technical and One-on-one Round.
How to prepare for SPRINKLR 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 SPRINKLR. The most common topics and skills that interviewers at SPRINKLR expect are Business Solutions, Data Analysis, Data Management, Forecasting and Machine Learning.
What are the top questions asked in SPRINKLR Data Scientist interview?

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

  1. what is attention in term of data scien...read more
  2. How would you judge the efficiency of L...read more
  3. How do you handle class imbala...read more

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SPRINKLR Data Scientist Interview Process

based on 4 interviews

1 Interview rounds

  • Coding Test Round
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SPRINKLR Data Scientist Salary
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₹17 L/yr - ₹30 L/yr
57% more than the average Data Scientist Salary in India
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