- Master’s degree: University of Washington, Washington
- Doctoral degree: Washington State University, Washington
- Data Scientist: Microsoft
- Data Scientist: Washington State University
A pet passion, two graduate degrees, and a chance encounter with the right person have led Michael Grant to the career path he’s on today — helping farmers better tend their crops through data.
Michael graduated in March from the Graduate School’s interdisciplinary Data Science Master’s Program. He came to this program after completing a Ph.D. in soil science at Washington State University, a degree that complimented his interests in agriculture, with a focus on land preservation for farmers.
While a full-time master’s student at the UW, he has also been working full-time at Microsoft on FarmBeats, which combines agriculture and data science to help farmers make more informed decisions about how to plant and tend to their crops.
So how did a soil scientist end up working at Microsoft? Michael became interested in data science as he began looking for work while preparing to defend his dissertation at WSU. He found an intriguing job in precision agriculture — which focuses on managing resources so the same crops can be reaped with less fertilization and water. From this, he recognized a professional niche he wanted to fill: bridging the gap between soil and quantitative sciences.
He lacked the coding skills he needed to do this work, so he started taking courses in statistics and coding as a non-matriculated student at the UW. Eventually he applied and was accepted to the UW’s Data Science Program, where he had the opportunity to learn about machine learning, database design and structure, statistics and coding in several languages.
Michael says the program was a great fit as someone who didn’t come from a Computer Science background, but was looking to build critical tech and AI skills.
And while many of these hard skills have proved valuable for his work at Microsoft, Michael says one of the most invaluable aspects of the degree were the opportunities for networking. He received advice and tips from professors who are experts in disease modeling, machine learning and other areas of interest, he says.
In fact, Michael’s internship at Microsoft was a direct result of networking. In the first quarter of his program, he met an employee from Microsoft at a career seminar. The employee was talking about a project that piqued Michael’s interest (which turned out to be Farm Beats). Michael gave the employee his resume; six months later, he was contacted about an internship opportunity.
He says his internship was a critical component of his experience in the Data Science program, and urges his peers in data science to seek out internships as much as possible. That’s what helps you stand out from an applicant pool and secure a job, he says.
And while his dissertation project was “very far from what I do now,” he says many of the skills are still very relevant. He learned to work through a problem and manage a massive research project. His background in soil also means that when scientists bring him data to analyze, it’s not just “numbers on a screen.” He has the deep domain knowledge that helps him translate the numbers into real life, which makes him stand out from other data scientists in his line of work.
Does he miss digging in the dirt as a soil scientist? Not exactly, he says.
“I like coding and working on the computer more than I liked being at the bench,” he says. His position at Microsoft is a great fit because it allows him to work on the agriculture projects he’s interested in without having to collect data in the field — one part of soil science work he always disliked.
But he still experiments with soil in his own backyard garden, where he grows tomatoes, kale, carrots and has experimented with indoor farming of lettuce and bulbs.
“I use soil testing to calibrate my own potting soil; that’s what came out of my Ph.D.,” he says with a laugh.
When his contract with Microsoft is finished at the end of June, Michael will be looking for more work in the data science world, preferably in a position related to agriculture.
Published June 2018