Lengthy process outline below: 1.) Written interview: A written interview divided into 3 sections. Engineering experience (13 Questions), education (9 Questions) and context (4 Questions). The Engineering section was mostly irrelevant because it was based on software engineering when this was a data science role. The Education section was lengthy but reasonable. The context section was reasonable as well. This took me around 5 hours to complete. 2.) IQ Test An hour long IQ test. No real way to prepare for this in my opinion. 3.) Take home skill assessment Coding project to complete on your own. This is very unstructured as you can use any tool you'd like. Took me around 3 hours. 4.) Interviews (Finally!) 4.1): Linux Interview (1 hr): The interviewer started out by prefacing that he thinks this interview is pointless for the role and he has no idea why he is doing this interview as it has very little to do with data science. Interviewer was super nice and I enjoyed the chat but I agree with him that this interview did not align with the skills required for the job. 4.2): General Coding Interview (1hr): This was a perfectly reasonable interview where we discussed general coding questions that relate to data science (Git, Databases, Joins). Good discussion. 4.3) Data Science Interview (45 mins): Interviewer showed up more than 15 minutes late so this was actually closer to 25 minutes long. In this interview it was clear to me that Canonical has no idea how to interview data scientists. The interviewer was thinking of nearly all the questions on the fly and did not understand most data science concepts that were in the job description so it was difficult to have a good discussion. Overall, I think Canonical could be a great place to work as everyone was really nice. However my interview experience was very negative. It seems like the data science team is new and they don't quite know how to deal with data science yet and that shows in the interview process. The interview process was overly lengthy and disorganized.