Today, Sean Durkin is the Head of Barclays' Data Solutions Center of Excellence. But when he started out as a data scientist in 2011, the field was in its infancy. Looking back, he realizes it was naïve to think university had given him everything he needed to survive the world of work.
In this week’s Business of Data podcast episode, he reflects on the culture shock many data scientists feel when taking their first steps into the business world and why the learning doesn’t stop when you leave university.
“There's still a great deal of learning,” Durkin says. “And it’s a different kind of learning, because you need to wrap your head around those non-academic, non-technical skills. For example, you need to learn to work with different personalities and to manage internal dynamics, such as people pulling in different directions.”
“With the help of great mentors, it became apparent to me that it doesn't matter what you can do technically, you still need to develop soft skills,” he argues.
Mentorship is widely regarded as a way to accelerate career growth. Durkin encourages upcoming data scientists to accelerate their professional development by finding a mentor, embracing failure and making the most of the opportunities their employers offer them.
He says: “If you’re fortunate enough to find yourself in an organization that offers mentorship or leadership programs, get on the programs! It's worth doing. And like anything worth doing, there’ll be hard work. But I promise you, when you look back, you’ll be glad you went through it.”
“If your company doesn’t offer mentorship, approach people several levels above you,” he continues. “If your goal is to reach a certain level of seniority, seek guidance from people above that level. Those are the people who’ll probably be on interview panels or the decision-makers for the role that you want to get into. Ask them what they look for in a leader.”
One of the biggest adjustments for those leaving university for the business world, Durkin says, is that there is no answer sheet at work.
“It’s quite a shift for people who are used to trying to solve problems that someone has already solved perfectly,” he reports. “[At university], your solution will be compared to the one in the textbook. If it’s not the same, you’re penalized somehow. So, you start off at work thinking your solutions will be torn apart.”
This mindset shift form perfectionism to one that prioritizes delivering some value quickly and improving things iteratively over time can be challenging for fledgling data scientists. But Durkin encourages anyone who may be finding this paradigm shift unsettling to be confident in the decisions they’re making and to remember they were hired for a reason.
“You won’t always know what the correct answer is,” he says. “Nobody has a crystal ball. But when you were hired, you were declared the best person for the job. It means I stand behind you and the organization is behind you.
“Expect to make mistakes. But when you look into the organization and you see how things are done, remember that someone made a decision for it to be that way. It’s your job; just go for it.”