By Tessa Xie, Senior Data Scientist at Cruise

Photo by bruce mars on Unsplash.

When I first made the transition from finance to data science, I felt like I was on the top of the world — I got a job in my dream field, my career track is set, I will just keep my head down and work hard, what could go wrong? Well, there were a couple of things… For the following year as a data scientist, there were several mistakes that I’m glad I caught myself making early in my career. This way, I had time to reflect and course-correct before it was too late. After a while, I realized that these mistakes are quite common. In fact, I have observed a lot of DS around me still making these mistakes, unaware that they can hurt their data career in the long run.

If my 5 Lessons McKinsey Taught Me That Will Make You a Better Data Scientist were what I learned from the best, the lessons in this article are those that I learned the hard way, and I hope I can help you avoid making the same mistakes.

Mistake 1: Seeing yourself as a foot soldier instead of a thought partner

Growing up, we have always been evaluated based on how well we can follow the rules and orders, especially in school. You will be the top student if you follow the textbook and practice exams and just put in the hard work. A lot of people seem to carry this “foot soldier” mindset into their working environment. In my opinion, this is the exact mindset that’s hindering a lot of data scientists from maximizing their impact and standing out from their peers. I have observed a lot of DS, especially junior ones, think they have nothing to contribute to the decision-making process and would rather retreat to the background and passively implement decisions made for them. This kicks off a vicious cycle — the less you contribute to those discussions, the less likely stakeholders will involve you in future meetings, and the less opportunity you will get to contribute in the future.

Let me give you a concrete example of the difference between a foot soldier and a thought partner in the case of model development. In the data collection and feature brainstorming meetings, the old me used to passively take notes on stakeholders’ suggestions so I can implement them “perfectly” later on. When someone proposed a feature that I knew we didn’t have data for, I would not say anything based on the assumption that they are more senior and they must know something that I overlooked. But…

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