Six Career Moves that Young Data Scientists Dismiss too Quickly as Boring
This blog is for people early in their careers as data scientists (or decision scientists or others using data, math, and algorithms to solve problems).
It is meant to be helpful if you are interviewing for your first job, deciding if you should switch companies, or even deciding if you are happy in your current role.
Don’t take this as saying what you should or shouldn’t do. There are many ways you can build a build successful career.
Instead, I’ll highlight some traps I’ve frequently seen early career data scientists fall into. These traps are the mistaken belief that some career moves may be too boring. It’s a trap because it can limit you. It can cause you to dismiss or never consider potentially promising opportunities.
Here they are:
One: “Using old technology is boring.”
This was inspired by a senior data scientist’s talk at Opex Analytics. She described how data scientists think in different phases of their early careers.
In school, they want to get the correct answer.
For their first job, they want a role that allows them to use and learn the latest technology.
Finally, and later, they realize that they should be using their skills to real problems.
Everyone can agree that solving real problems is important. The nuance is that working hard on something no one uses is demoralizing.
That is, it may be true that working in older technology is boring. However, working on something that no one uses is even more boring.
So, don’t dismiss opportunities to solve real problems and have an impact, even if it means you use older technology.
Two: “Doing the same thing is boring.”
I’ve seen many data scientists feel like they aren’t learning new things if they have to do something similar multiple times.
There is another way to frame this that highlights the trap: “Doing the same thing many times allows you to develop expertise.”
Being an expert gives you a lot of satisfaction and career advancement. And you are learning. You are going deep in one area.
It isn’t wrong to want to learn and see many different problems and technologies. Since data scientists like to learn, they tend to overweight the importance of the new and underweight the value of becoming a domain expert.
Three: “Sales is boring (or icky).”
I’ve spent much of my career in and around enterprise software and consulting sales. I think technical people avoid it too quickly.
First, if you want to start a company, join a small firm, or take larger leadership roles, you’ll want to understand sales.
Second, I think technical people don’t understand two very different roles on the sales team.
The first role is the quota-carrying sales rep. To simplify, this job involves building relationships, navigating the client organization, navigating your organization, and signing the business. This is the role most technical people think about. Most technical people shouldn’t take this job.
However, technical people underestimate another sales job: the technical sales or pre-sales role. This person is part of the sales team. They are responsible for understanding the client’s problem, demoing the solution with the client in mind, and answering technical questions.
Technical people dismiss it because they think they won’t learn anything or they don’t think they’ll like sales. I found technical sales intellectually interesting. You see a lot of different problems, you have to come up with solutions to those problems quickly, and you need to demonstrate your product in the right way (to show it in the best possible light and to set up your company for success if you win the business). And there is a thrill to it— you often have a very short time to show your work, the clients test you, and you get real feedback from wins and losses.
You may later find out that the sales team is not for you, but you will have learned valuable lessons you can take with you.
Four: “Management is boring.”
Many data scientists think management is boring— it is all about meetings and PowerPoint slides.
OK, there is some truth to this. When I was a manager at IBM, one of my bosses told me this joke: “What is the happiest day in the life of an IBM manager? When they become an individual contributor again!”
Since many organizations have technical and management tracks, data scientists tend to overestimate their desire to stay on the technical track forever.
However, I have watched data scientists over long periods. At some point, most want more from their career or feel stuck. A management track is a way to get unstuck, create opportunities for others, and have a larger impact. But, yes, there will be meetings and PowerPoints.
Given this, you should think about developing management skills along the way. When you are ready, it will be an easier jump.
Five: “Not getting a good title is boring (or very troubling)”
I’ve seen young data scientists put too much weight on titles. They too often use titles to compare job offers or to decide if they should switch companies.
When weighing job offers, I advise data scientists to ignore the title. Instead, focus on the job, what they will be doing, what skills they can learn, etc.
When deciding whether to switch companies, I give the same advice: Ignore your title or lack thereof. There are plenty of good reasons to leave for another company, but the title shouldn’t factor into it.
My typical line is that when you are in your 40s and 50s, you will have no idea what job titles you had in your 20s. If you have good jobs that are challenging you, opportunities will come.
Based on my experience, almost everyone I’ve given this advice has ignored it. Maybe I’m wrong, or maybe by the time it comes to this, it is really about more than the title.
Six, “Working too much is boring (or not something I want to do).”
I didn’t know if I should include this. There are so many circumstances that go into this decision.
However, quite a few job types are known for long hours: top-tier consulting firms, financial analysts on Wall Street, and intense start-ups. The trade-off is worth thinking about.
The benefits of working fewer hours are well-known. Here are two lesser-known benefits on the other side.
First, working long hours in your 20s can pay dividends for the rest of your career. Learning more, seeing more, and developing more skills gives you a great base to grow on.
Second, there is an unexpected good feeling and tight friendships you build when you work very hard on something very difficult and impactful. Brie Wolfson made this point about an early time at Stripe. This is similar to what Sebastian Junger pointed out in Tribe.
Note: An employer could use these tips to help recruit, retain, and better develop data scientists within their organization.