Iris Software Data Science Engineer Interview Questions and Answers

Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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Q1. Questions were asked related to ML modelling process
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Q2. Basic python coding
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1.Describe your project.
2.Explain machine learning concept.

Presentation based question on case study

Q1. Tell something which you haven't put in your resume
Ans. 
I have experience in organizing and leading community service projects
• Organized a charity event to raise funds for a local animal shelter

• Led a team of volunteers in cleaning up a park in the neighborhood

• Coordinated a food drive for a homeless shelter
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Aptitude, coding on python NLP

Python Data Frames, String list manipulation

Q1. Common discussion
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Q2. Salary location etc
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Q1. What is Transformers and how they work
Ans. 
Transformers are models used in natural language processing tasks, known for their ability to handle long-range dependencies.
• Transformers use self-attention mechanism to weigh the importance of different words in a sentence.

• They consist of encoder and decoder layers, with each layer containing multi-head attention and feed-forward neural network.

• Examples of transformer models include BERT, GPT-3, and TransformerXL.
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Q2. How to train a model with imbalance data
Ans. 
Use techniques like oversampling, undersampling, SMOTE, or ensemble methods to train a model with imbalanced data.
• Use oversampling to increase the number of minority class samples.

• Use undersampling to decrease the number of majority class samples.

• Use Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.

• Utilize ensemble methods like Random Forest or Gradient Boosting to
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Write a code for Factorial series

Q1. Deep learning related
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Q2. Maximum GenAI Based question
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Test+2 interviews(technical and statistics)

Basic guestimates and case studies

Platform HackerEarth
9 Questions
2 Coding Questions on Python based on Dynamic Programming
7 Questions MCQ on Data Science and SQL

Q1. Data Science Projects discussion Python Coding by sharing screen
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Q1. Data Science Projects Detailed Questions
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Q2. In Depth Knowledge on any one of your project.
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Q3. Cross Questioning on Regression, Classfication Problems. Dummy Variable Trap Statistics Questions
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Q4. Questions on Deep Learning Models working and architecture like RNN Exploding/Vanishing Gradient Problem, Solutions like LSTM, CNN model working and different terminologies used.
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Q1. Again this round was pure Technical. All questions were on Data Science Projects and scenario based. 2 Questions on Probability and puzzle based.
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Q1. Final Round Discussion about Company's Policies and Roles. Your Expectations from the company, the kind of role you are looking for and why Tiger.
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