Honeywell Technology Solutions Data Scientist Interview Questions and Answers

Basics and Fundamentals

Q1. Reason for job switch
Ans. 
Seeking new challenges and opportunities for growth in a different environment.
• Looking for a more challenging role to further develop skills

• Interested in exploring new technologies and methodologies

• Seeking a better work-life balance or company culture

• Opportunity for career advancement or higher salary

• Desire for a change in industry or focus area
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Q2. Work Culture Expectations
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2 coding questions - one on binary tree and list operations.

Q1. Xgboost explanation
Ans. 
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• Xgboost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees.

• It is widely used in Kaggle competitions and other machine learning tasks due to its efficiency and accuracy.

• Xgboost is known for its ability to handle missing data, regularization techniques, and parall...read more
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Q2. Data structures question compulsory
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Q1. Explain L1 & L2 regularization
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L1 & L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regularization adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization encourages sparsity in the model, while L2 regularization tends to shrink the c...read more
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Q2. Explain error metric
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Error metric is a measure used to evaluate the performance of a model by comparing predicted values to actual values.
• Error metric quantifies the difference between predicted values and actual values.

• Common error metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.

• Lower values of error metric indicate better performance of the model.

• Error metric helps in und...read more
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Q3. Where do you see yourself
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Q1. Resume related questions on project experiences
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Q2. Check technical stack, whether you have the right tech skills
Ans. 
Yes, I have the right tech skills for the Data Scientist role.
• Proficient in programming languages like Python, R, and SQL

• Experience with data visualization tools like Tableau or Power BI

• Knowledge of machine learning algorithms and statistical analysis techniques

• Familiarity with big data technologies like Hadoop and Spark
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Q1. Basic ml concept
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Q2. Details of machine learning projects and how to deal with communication, prioritization issues
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Simple leetcode type sql, python questions

Predictive modeling project

Q1. Explain the project and that related questions only.
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Q1. Why Microsoft? What products do you use?
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Q1. Discussion on projects written in resume and related problems. Some stats
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