Updated: Mar 28, 2019
A few months ago I applied for a new job. This was the first one I'd seen in a couple of years that actually piqued my interest. I exercised one of the perks that comes with my Canadian PR - I can now actually explore these opportunities and am not bound to my sponsoring company. Let me clarify - I'm quite happy in my current position, but the new thing offered a few perks that I've been chasing for a while. Opportunity to hire and lead a team, check. New domain, check. Challenging problems, less travel and more opportunities for learning, check, check, check. Given I've never been in the position of interviewing while being comfortably employed, I figured I had nothing to lose and was determined to treat it as a learning opportunity. Boy did I learn some things.
The first few rounds of interviews were pretty straightforward, and I think I did quite well. It became fairly clear that the company was standing up a Data Science capability in a new city, with a narrow problem scope that they wanted to address. It also became clear that the level of knowledge of the data and analytics field was limited within the company. I answered questions that I'd asked new candidates when recruiting them to my current organization. If I had trouble answering those, I reasoned, I had no business being in my current position! The final round was to be scheduled at their HQ and would consist of about 4 hours of interviews with various members of the extended team. Pretty standard, and travel arrangements were made.
Life is busy, and I didn't prepare anywhere near as much as I could have. I figured I'd see how I could do going in cold and that would give me a sense of what things I needed to brush up on. My flight was delayed by 1 hour, so I reviewed a few topics in the time this allowed - mainly machine-learning related topics that I knew they would ask about in at least some detail.
Once on-site, the interviews began. The first two sessions were with software engineers and QA folks and again, I think they went reasonably well. I've been exposed to a lot of software engineering best-practices through previous roles and I can talk comfortably at a level higher than your typical data scientist on those topics. Where things really started to go sideways was the lunchtime meeting with the CTO. Right off the bat I got a strange vibe off of the guy. I was expecting to be grilled fairly hard, but he was basically throwing keywords at me, asking me to explain, and then cutting me off mid-sentence to tell me his thoughts. Maybe he was just excited about the topics, but it was weird. He was also very focused on deep learning, a topic with which I don't have as much familiarity as I would like. I was very upfront through the entire process about this, along with the fact that I've taken some steps recently to address it. I've been to conferences and attended learning groups internally but I am by no means an expert. That clearly wasn't the answer he was expecting.
Then I really dropped the ball. It was a stats 101 question that I flubbed spectacularly. Fresh graduates should know the answer. I should definitely know the answer. I've taught this stuff to high-school kids for crying out loud. Having been blindsided by his strange mannerism and the consistent question-interruption cycle, I totally blanked. It was game over and we both knew it. We finished our lunch in awkward semi-silence and made our way back to the office.
He continued with the questions once we returned to their office. Things came to a head when it was implied that I wasn't doing enough to learn more about emerging fields such as deep learning. Why was I waiting around to have the opportunity to learn about this at work, rather than spending my own time pursuing these topics? I reiterated the fact that I'd attended a conference just three weeks ago on this exact topic, and that I'd participated in internal learning groups. This still didn't seem to be a satisfying answer. It was then that I was brutally honest, in an attempt to figure whether the weird vibe I was getting was warranted. What did I have to lose? I'd already sealed my own fate with my inability to answer the basic stats question from earlier. I wanted a peek inside this man's head. I spoke about the passing of my mother 6 months prior and how I haven't had time or motivation to take part in anything extra-curricular in the intervening months. How I'm re-assessing my career path in light of this fairly major life event. His response? "Sounds like you're having a mid-life crisis", said with a laugh.
Frankly it took every ounce of restraint in my body not to simply walk out. I had my answer - this was the last person I'd want to be working closely with or for. He quickly realized what he'd said and tried to walk back his comment, but it was too late. I finished the final round as quickly as I could and was on my way. I contemplated rescinding my candidacy that night, but figured the rejection email would be following shortly afterwards. Sure enough, it arrived a few days later. They asked for feedback. I gave none as I didn't see the point. I thanked them for their time, told them I appreciated the learning opportunity. And what a learning opportunity it was.
My key takeaways:
I need to brush up on my stats 101 content. Just because I'm not exposed to it every day doesn't mean it's not important. Something like tutoring might help with that. This one was 100% on me.
Deep learning is a thing. The AI label is a marketing goldmine and everyone wants in, despite it's well-documented shortcomings. I need to get on top of this topic. Another opportunity for self-improvement.
You are interviewing a prospective employer as surely as they are interviewing you, especially so when you are already happily employed. Make sure you're scanning for your own red flags as you're trying to impress with your knowledge
Don't be afraid to simply cut your losses and remove yourself from a bad situation. If I had my time over, I would have left after the offensive comment, thanking them for their time, and saving the final interviewers the trouble (the final duo were lovely guys, for what it's worth)
Employer expectations in our field vary wildly and are often laughably unrealistic, especially for organizations that are immature in terms of analytics. This company wanted someone with a depth of understanding of everything "Data Science", as well as the know-how to implement the full life-cycle of solutions, additional to the ability to think strategically and plan a long term roadmap. Good luck finding that one person - that's why you build a team.
Cover image: https://www.intervies.com/2018/07/10-most-common-reasons-for-failure-at.html