Data Scientist applicants have rated the interview process at Honeywell with 3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 85.7% positive. This is according to Glassdoor user ratings.
Candidates applying for Data Scientist roles take an average of 21 days to get hired, when considering 1 user submitted interviews for this role. To compare, the hiring process at Honeywell overall takes an average of 13 days.
Common stages of the interview process at Honeywell as a Data Scientist according to 1 Glassdoor interviews include:
Presentation: 20%
Phone interview: 20%
Other: 20%
Personality test: 20%
One on one interview: 20%
Here are the most commonly searched roles for interview reports -
I applied through university. I interviewed at Honeywell in Feb 2017
Interview
I was selected from university recruiting and interviewed the following day. The interview was solely based on projects on my resume. Interviewers were friendly , i would not say it was a tough interview. Got a followup mail of further considerations and there was no updates after that.
So basically I still am not sure if i was selected or rejected.
Interview questions [1]
Question 1
What are the challenges you face during the projects?
I applied in-person. I interviewed at Honeywell (Atlanta, GA) in Apr 2025
Interview
3 rounds (at least to my knowledge) - > puzzle like assessment, interview with one of the managers, and then technical round assessment
the call with the managers mainly talks about your resume, your projects (know your projects through and through aon what to improve, each ML technique, etc)
Interview questions [1]
Question 1
if you were to use a different machine learning technique which could you have used, and asked my questions based on the ml technique being used
I applied through a recruiter. I interviewed at Honeywell (Bengaluru)
Interview
The interview process was seamless from start to finish. It included multiple rounds of technical and behavioral interviews. The interviewers were professional and provided clear expectations. Feedback was timely and constructive. Ultimately, the process culminated in a successful job offer.
Interview questions [1]
Question 1
Statistical and Probabilistic Reasoning
Explain the difference between correlation and causation.
This question assesses your understanding of fundamental statistical concepts and your ability to apply them in data analysis.
Machine Learning
How do you handle imbalanced datasets?
This question tests your knowledge of common challenges in machine learning and your approach to addressing them.
Data Munging and Exploration
You have a large dataset with missing values. How do you handle them?
This question evaluates your data preprocessing skills and ability to deal with real-world data imperfections.
Model Evaluation
Explain the difference between precision, recall, and F1-score.
This question tests your understanding of key performance metrics and when to use them.
Business Understanding and Problem Solving
How would you approach a problem where you need to predict customer churn?
This question assesses your ability to apply data science to a real-world business problem and develop a solution strategy.
there are 4 interview rounds: 1. Screening round where they ask basic programming questions, ML Algorithms, Deep learning, NLP basic questions. Also some scenario-based questions. The average interview time was 45-60 mins.
Interview questions [1]
Question 1
Sorting algorithms, working of ML models like Random Forest. Difference between bagging and boosting. What are activation functions?