Faster pace, immediate impact: Transitioning from academia to a data science job at Adobe

May 14, 2014

FangpoWangWhen Fangpo Wang graduated from Duke University with a PhD in statistics, she had two options: remain in academia or put her skills to work for a company. She was mulling those choices when she came across a job posting for a data scientist at Adobe.

“When I was in the PhD program, I thought Adobe was just making creative software,” Fangpo says. “I didn’t know about the data science work.”

That’s when she learned about Adobe’s growing digital marketing business, which creates and provides advanced marketing analytics software for some of the biggest companies in the world.

Fangpo’s field of study was pretty specific: space-time modeling of directional data. But the Adobe job posting called for everything that she had spent her academic career studying: deep knowledge of statistics and developing quantitative models to address interesting problems.

For Fangpo, the opportunity to do that kind of work was enticing enough to pull her away from academic life.

“There’s a much faster pace here. In academia, I spent a long time developing a methodology, and it often took years to see the impact” Fangpo says. “But at Adobe, you have more interesting real-world problems to solve and your work can go into the product quickly, so you can see the immediate value.”

The Adobe Marketing Cloud helps clients understand how to spend their budgets across search marketing, social marketing, display advertising, and even print and TV ads. Fangpo and the data science team at Adobe create the brains of the Marketing Cloud, developing algorithms that can analyze and make sense of massive amounts of complex data about marketing campaigns and customer actions.

While academic work has its own charm, it’s often done in isolation—late nights of studying, hundreds of hours spent slogging away at a thesis—Fangpo says the collaborative environment at Adobe has been a gratifying change of pace.

“I work alone and as part of a team, so it’s very good balance. We also have weekly meetings where we sit down together to ask each other questions, share our thoughts about ongoing projects, and help each other with problems,” she says. “We also get to share knowledge that we have learned that might be helpful to someone else.”

The varied backgrounds of people on the team—statistics, engineering, computer science, applied math, physics, and even finance—ensure that there’s always a new perspective on just about any problem. And that means Fangpo is finding work in the industry to be just as educational as academia.

“I have access to more mentors than I thought I would,” she says. “And it doesn’t matter if you don’t have experience working with digital marketing data—most people coming from school don’t. But if you are open-minded and a big thinker about interpretation of modeling, it’s a great place to be.”

We’re hiring:

We’re currently hiring for a variety of data scientists positions at Adobe. Explore our career site for a full list of open positions.