Data Analyst Intern applicants have rated the interview process at John Deere with 2.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 67% positive. To compare, the company-average is 75.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Data Analyst Intern roles take an average of 42 days to get hired, when considering 3 user submitted interviews for this role. To compare, the hiring process at John Deere overall takes an average of 25 days.
Common stages of the interview process at John Deere as a Data Analyst Intern according to 3 Glassdoor interviews include:
Presentation: 25%
Skills test: 25%
Group panel interview: 25%
Background check: 25%
Here are the most commonly searched roles for interview reports -
sent an OA outlining basic data science questions and coding questions, then followed up with review and technical + behavioral interview with a manager to go over OA and other conceptual questions
Interview questions [1]
Question 1
Describe a machine learning project you have worked on. What challenges did you face?
I applied through a recruiter. The process took 6 weeks. I interviewed at John Deere (Champaign, IL) in Sep 2023
Interview
Got interview chance on school's career fair, first round is behavioral using STAR method to answer questions from two interviewers. second round is a hackerrank test on python and SQL, you need to present your result to the interviewer. Verbal offer happened one week after the interview and official offer letter follows another week.
the interview was in two parts - Behavioral Interview and Technical Assessment. The Behavioral was as expected - two data scientists basically ask you questions about your experiences. The technical assessment was a hackerrank OA. The questions were more or less pulled from "intro to statistical learning," so give that a read through and your good
Interview questions [1]
Question 1
behavioral - "tell me a time when you had to work in a team" or something along those lines technical - what kind of feature engineering would you do on this particular dataset and/or what features would u think are the most influence the machine learning model the most