In the tech-savvy world we live in, my journey began with a degree in Software Engineering and then a master’s in Data Science. These fields are not just about coding and numbers; they’re about solving puzzles and making discoveries. As I stepped into the professional world, I met many managers. Each one had their own way of doing things, and sometimes, it was hard to get our ideas to match, especially since I always had my own way of looking at problems.
One day, I had a meeting with our CEO. He spoke with such confidence that it made me think. You see, as a data scientist, I was taught to always question things and never be too sure because there’s always a chance we could be wrong. That’s why I decided to learn about management too. It took a while, but I started to see things from my manager’s point of view.
Being a manager is different. You have to be sure of what you’re saying, speak clearly, and make people believe in your vision. This is where I saw the first big difference between a data scientist, who is always checking the numbers, and a manager, who leads with confidence.
We data scientists get really excited when our models predict something correctly or when we find a pattern nobody else has seen. But we also know that our models might not be perfect, and there might be a better solution out there. We’re always thinking about how to improve our work.
I remember working on a project about road safety. We had to guess where accidents might happen, and it was really important to get it right because we were talking about accidents that could hurt people badly. The problem was, we didn’t have enough data, and it was hard to make good predictions. It was a confusing time because making a wrong prediction could lead to something really bad. I had to be careful about how I talked about our predictions. I had to show that even though our numbers looked good, it didn’t mean we could stop being careful.
Thankfully, my experience with smart mobility helped me combine two different ideas to come up with a better solution. By predicting accidents, we could help prevent them, even if we weren’t completely sure. It turned out to be one of the best projects I’ve ever worked on.
So, if I seem cautious with my solutions, it’s because that’s how I’m trained to think as a data scientist. But if my manager sounds super confident and says we’re the best, it’s because that’s their job. And the truth is, the hard work and dedication we put into our jobs do make us one of the best in the world.
This stroy shows that as a data scientist who also has some management background, in order to manage a situation where different perspective come into play, experience can be the best tool to handle the situation. It is all about combining the careful analysis of data science with the leadership point of view. This is the skill that we need as analytics translator. We are not only needed in any project and business because we are good at what we do, analytics role is crucial in any company because we care, we are thorough, and we dare to merge the percisio of data with confidence of leadership point of view. That is the heart of the innovation, and it is why we are making a difference in smart mobility and beyond!
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