Colton, can you describe your journey into data science?
After earning my Ph.D. in Mathematics in 2008, I was a faculty member in mathematics departments for 12 years. At the same time, I was working with an IT consulting company on data science and project management. All of that, in concert with my Omdena project, came together to enable my current role.
How long did it take you to gather a substantial amount of skills/ knowledge?
If you look at the time that passed, it looks like it took quite a long time. The reality is that I’ve been interested in programming and AI for many years and honing my skills has been an ongoing process throughout. Most of the credit should really go to a strong and sustained level of curiosity. As a result, this drove me to constantly study new approaches and technologies as they emerge and apply them (whether an immediate application exists or not) to solidify my understanding.
How can a senior data scientist make the best out of the Omdena experience?
Just about any job opportunity is looking for experience. That can come from working on projects or leading projects, but experience in a healthcare project, for example, would not carry as much weight for a pricing opportunity. This leads me to one of the biggest advantages that Omdena (and indeed also consulting) offers, which is a diversity of available projects. Thus, allowing me to have meaningful conversations in any interview situation. One particularly important advantage that Omdena has over consulting is the perspective of social impact. Every company wants to turn a profit, but the better ones put their heart into making a difference in the world. Here, the Omdena experience helps match strong and experienced data scientists with those impactful companies.
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If you would start all over again, what would you do differently? What tips would you give to your younger self?
Avoid more of those rabbit holes. Curiosity is a valuable asset that I mentioned above but can lead to an exploration of many time-consuming dead ends. I know full well that I’ve learned a lot from exploring those dead ends, partly by solidifying my understanding of why those ends are dead, but did I mention “time-consuming”?
Any closing words?
The only additional point I would like to clarify is that I cherish my experience with Omdena, particularly as a Lead machine learning engineer for the huge opportunities and the truly inspiring conversations that it has led to, and for the priceless relationships that have come out of it. I’m thankful for my Omdena experience and only wish there were more hours in the day (especially lately) to be able to contribute more!