The other day I had got a question I haven’t addressed here on this list. It was from someone who is a mid-career techie who is looking to transition to data science.
My answer is, of course, important for others in the same situation but also relevant to younger scientists and engineers just starting their careers.
Older employees generally have several advantages. Among them is a proven track record of handling tasks in a real-world environment.
These include the ability to take sometimes-vaguely-worded requirements and translate them into concrete technical problems that are both simple enough to be feasible and sophisticated enough to be useful.
I once worked with a bright scientist who had a Ph.D. in physics from the world-renown MIT. Once, he was given a signal processing problem that took him months to complete. In the end, he submitted a huge, very technical report that read more like a dissertation. He even included an Acknowledgements section!
He was fired because of this.
His manager said, “It takes Joe forever to come up with a solution, and when he does you can’t understand it anyhow.”
Another trait of experienced scientists is the adaptability needed to respond to changing requirements. In classes, you are given the problem stated in concrete terms and it never changes. In the working world, you’ve got to be willing to toss out what you’ve been doing and immediately jump to another approach.
The skill to communicate results to an audience of widely-variable technical sophistication ought to be obvious.
I want to draw your attention to two things.
First, there is nothing that I stated above that is specific to data science. For those mid-career scientists and engineers wondering if they can transition into our field, you should be keenly aware of these strengths of yours.
Second, there is no law of physics, psychology, or biology that says younger techies can’t also develop these qualities. Do so, and you’ve got an enormous advantage over others competing for the same jobs.
The keys are to (a) be aware that these traits are vitally important, (b) develop these qualities in yourself, and (c) make it very clear to prospective employers that you possess these skills.
Look, I enjoy learning about new machine learning techniques as much as the next data scientist, but I’m also keenly aware that doing so only makes me marginally more useful to my employer.
If you have job experience—even if it’s in another field—make a strong case for yourself.
If you are looking to get your very first job then you should take on personal projects. When you are telling employers about it, you want to highlight occasions when you treated it as more than a glorified course assignment.
There’s room for data scientists of all experience levels and technical knowledge in our field. The key is knowing your strengths (and weaknesses!) and crafting your job search strategy around them. That’s one of the reasons I emphasize starting with strategy instead of tactics (e.g., updating/creating your resume).
To your success,