Angela Bassa is the director of data science and head of data science and machine learning at iRobot, a technology company focused on robotics (you might have clean floors thanks to a Roomba). Prior to joining iRobot, Angela wore several different hats, including working as a financial analyst at Morgan Stanley, the senior manager of big data analytics and platform engineering at EnerNOC, and even a scuba instructor in the U.S. Virgin Islands.

Join Angela and I as we discuss the role data science plays in robotics and explore:

Why Angela doesn’t believe in a division between technical and non-technical skill

Why Angela came to iRobot and her mission

What data breadcrumbs are and what you should know about them

The skill Angela believes matters most when turning data science into a producer of decision support

Why the last mile of the UX is often way longer than one mile

The critical role expectation management plays in data science, how Angela handles delivering surprise findings to the business, and the marketing skill she taps to help her build trust

Resources and Links

Twitter: @AngeBassa

Angela’s Website

iRobot

Designing for Analytics Seminar

Quotes from Today’s Episode

“Because these tools that we use sometimes can be quite sophisticated, it’s really easy to use very complicated jargon to impart credibility onto results that perhaps aren’t merited. I like to call that math-washing the  result.” — Angela

“Our mandate is to make sure that we are making the best decisions—that we are informing strategy rather than just believing certain bits of institutional knowledge or anecdotes or trends. We can actually sort of demonstrate and test those hypotheses with the data that is available to us. And so we can make much better informed decisions and, hopefully, less risky ones.” — Angela

“Data alone isn’t the ground truth. Data isn’t the thing that we should be reacting to. Data are artifacts. They’re breadcrumbs that help us reconstruct what might have happened.” — Angela

[When getting somebody to trust the data science work], I don’t think the trust comes from bringing someone along during the actual timeline. I think it has more to do with bringing someone along with the narrative.—Angela

“It sounds like you’ve created a nice dependency for your data science team. You’re seen as a strategic partner as opposed to being off in the corner doing cryptic work that people can’t understand.” — Brian

“When I talk to data scientists and leaders, they often talk about how technical skills are very easy to measure. You can see them on paper, you can get them in the interview. But there are these other skills that are required to do effective work and create value.” — Brian