Data Scientist applicants have rated the interview process at RTX with 4 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 33% positive. To compare, the company-average is 75.4% 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 3 user submitted interviews for this role. To compare, the hiring process at RTX overall takes an average of 29 days.
Common stages of the interview process at RTX as a Data Scientist according to 3 Glassdoor interviews include:
IQ intelligence test: 22%
Phone interview: 22%
Presentation: 22%
Group panel interview: 11%
One on one interview: 11%
Skills test: 11%
Here are the most commonly searched roles for interview reports -
I applied online. I interviewed at RTX (Bengaluru) in Jan 2023
Interview
The interview process is a multi-stage process for hiring new employees. The interview process typically includes the following steps: writing a job description, posting a job, scheduling interviews, conducting preliminary interviews, conducting in-person interviews, following up with candidates and making a hire.
I applied through other source. The process took 3 weeks. I interviewed at RTX
Interview
Excellent interview process. Each step is defined and communicated well with the candidate. The data challenge was very interesting and fun. I really enjoyed the process and meeting all the people.
Interview questions [1]
Question 1
what kind of technology that you don't want to work on?
2 Phone screens, take home project and full day onsite interview. Very smart people on the team and overall positive experience, but they grill you very hard on everything you have put on your resume so be prepared to derive many technical topics from scratch with no references.
Interview questions [1]
Question 1
Final round interview was quite difficult. Was asked to derive Fourier transforms and various probability distributions form scratch and speak about physical interpretation of various parameters (in addition to being grilled on over 20+ machine learning topics)