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ElectrifAi Analytics Specialist Interview Questions and Answers

Updated 31 Aug 2015

ElectrifAi Analytics Specialist Interview Experiences

1 interview found

Interview Preparation Tips

Round: Test
Experience: The test had two parts, the first part consisted of 20 objective aptitude questions and the second part had 5 subjective questions, which were of moderate level of difficulty. 30 people were shortlisted for the final round of interviews.

Round: technical interview
Experience: The first interview was mostly related to my resume and the projects related to statistics that I had done during internships. He also asked me some questions related to regression analysis followed by a simple puzzle.

Round: Puzzle Interview
Experience: The second interviewer asked me two- three puzzles and then we had some discussion related to my interest in data science.

Round: case study interview
Experience: In third interview he asked me two puzzles followed by a case. The interviewer was very helpful and he only wanted to see my approach.


Round: technical interview
Experience: The final interview was a telephonic interview with a senior analyst. He asked me some questions on my internship projects. I was also asked a guess estimate problem.

General Tips: Do’s and Don’ts :

Concentrate on at most two sectors because it is very difficult to prepare for more than two sectors. Also, do not write anything which you are not sure about in your resume because you may feel that it would be helpful for getting shortlisted but considering that your resume would drive your interviews so it is very necessary that you are confident about the things in your resume. Placement Experience (write in different section for different companies)


Final Tips :
Start preparation as early as you can. Decide what kind of work you would like to do and then decide what sectors you want to prepare for. Be in touch with the seniors who are working in the firms that you are targeting. Their advice would be very helpful. Also do some mock interviews in wing or attend the workshops conducted by SPO.

College Name: IIT Kanpur

Interview questions from similar companies

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

(1 Question)

  • Q1. Related Python Questions on Data Science
  • Ans. Brush up your knowledge on pandas numpy scikitlearn
  • Answered Anonymously
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
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 

(2 Questions)

  • Q1. Technical interview related to my projects and assignments
  • Q2. Difference between supervised and unsupervised learning, k means clustering, knn, SQL joins
  • Ans. 

    Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data. K-means clustering is a type of unsupervised learning algorithm. KNN is a supervised learning algorithm. SQL joins are used to combine data from multiple tables.

    • Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data

    • K-means clustering is a type of unsupervised learning...

  • Answered by AI

Skills evaluated in this interview

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ElectrifAi Analytics Specialist Reviews and Ratings

based on 2 reviews

1.0/5

Rating in categories

1.7

Skill development

3.6

Work-life balance

2.3

Salary

1.7

Job security

3.6

Company culture

1.0

Promotions

3.6

Work satisfaction

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