Data Science Career Track mentor David Yakobovitch has been a teacher of some sort nearly all his life, stretching back to seventh grade.
“I had this great math teacher, Harriet Rubinstein,” he said. “When I struggled on material, she was always there to take time after class to help me learn, to mentor me, and really motivated me to get involved in math competitions to help practice the skills we were learning.”
David started tutoring students after school in middle school and continued through high school. “All of that really helped me gain an appreciation for both learning and mentoring,” he said. “It’s something that’s stuck with me all along the way.”
For the past two and a half years, David has worked with the Software Carpentry Foundation, teaching nonprofit data science and computer science workshops to postdoc students. Through this program, he’s taught at the University of California at Merced, West Virginia University, Rutgers, NASA, and Harvard Medical School.
More recently, he has worked with General Assembly, and, beginning last fall, with Springboard.
One key to working with students, particularly on bigger capstone projects, is flexibility: “Some of my mentees tell me, ‘David, I’m in it to win it. I’m fully committed, I’m spending 40 hours a week.’ And I say, ‘That’s great, let’s pick a more challenging project.’ And some students are working two jobs, have a family, and could only commit eight hours a week. I let them know that’s OK and we need to be more realistic with process and workload because if you don’t have as much time, I don’t want to set you up for failure, you don’t want to set yourself up for failure.”
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Among David’s proudest professional accomplishments, outside of mentoring, has been his work with DataKind, which brings together data scientists to provide social organizations with pro bono help to tackle humanitarian issues in the fields of education, poverty, health, and human rights, among others.
Last fall, David and a team of about 20 data scientists, business intelligence analysts, and data analysts worked with the Los Angeles mayor’s office on an affordable housing problem. The city has rent control and just-cause eviction protections, leading many owners of rental properties to bemoan their inability to collect higher rental revenues. Under certain conditions, owners can invoke the Ellis Act to remove an entire building from the rental market. This allows them to repurpose the building and convert it into condominiums available for sale, thereby improving profit margins. The consequence is that affordable housing stock can become depleted, leaving many lower-income families in a bind. Owners can do this legitimately, of course. But sometimes they skirt the law as they go through the process. Fines are relatively low and by the time the city notices, little can be done.
The DataKind project’s goal was to use data collected during inspections, such as permit requests, to predict which building owners might be on a path toward Ellis Act evictions without going through the proper channels.
“With the early warning system, we were able to identify over 125 properties that we thought within the next three months, with a 95 percent confidence, were going to be at risk,” David said.
The system was immediately implementable and, armed with the right information, actionable.
Currently, David is wrapping up a one-year fellowship program with Columbia University in partnership with Fuse Machines. To become excellent at data science, David believes he needs to not only master what is currently in practice, but also to stay on top of what’s on the horizon—and particularly what is coming with artificial intelligence (AI).
After finishing his fellowship, David plans to continue teaching to help empower the next wave of data scientists and AI researchers. He sees many industries evolving as they become more influenced by data science, and a growing need for talented individuals to take on these kinds of roles.
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