008 - Dr. Puneet Batra (Assoc. Director, Machine Learning at Broad Institute of MIT and Harvard) on aligning data science with biz objectives, user re...

Dr. Puneet Batra is the Associate Director of Machine Learning at the Broad Institute, where his team builds machine learning algorithms and pipelines to help discover new biological insights and impact disease. Puneet has spent his career stitching together data-driven solutions: in location data analysis, as cofounder of LevelTrigger; in health care, as Chief Data Scientist at Kyruus; as lead Analytic Scientist at Aster Data (Acq by Teradata); and in fundamental models of particle physics, developing theories for Fermilab’s Tevatron and CERN’s Large Hadron Collider. He has held research positions at Harvard, Stanford and Columbia Universities. Puneet completed his BA at Harvard University and has a Ph.D. from Stanford University.

 

 

A friend of mine introduced me to Puneet because he was kicking off a side project using machine learning to dig into what creativity is through the lens of jazz. Since Puneet is not a musician by training, he was looking for some domain-specific knowledge to inform his experiment, and I really liked his design-oriented thinking. While jazz kicks off our conversation, we went a lot more deeply into the contemporary role of the data scientist in this episode including:

 

 

• The discussions that need to happen between users, stakeholders, and subject matter experts so teams get a clear image of the problem that actually needs to be solved.
• Dealing with situations where the question you start with isn’t always the question that is answered in the end.
• When to sacrifice model quality for the sake of user experience and higher user engagement (i.e. the “good enough” approach)
• The role of a data scientist in product design.

Resources and Links:

Puneet Batra on Twitter

 

Puneet Batra on LinkedIn

 

Quotes from Puneet Batra

"Sometimes, accuracy isn't the most important thing you should be optimizing for; it’s the rest of the package…if you can make that a good process, then I think you're more on the road to making users happy [vs.] trying to live in this idealized world where you never make a mistake at all."

 

"The question you think you're answering from the beginning probably isn't the one that you're going to stay answering the entire time and you've just got to be flexible around that."

 

"Even data scientists and engineers should be able to listen with empathy and ask questions. I got a good number of tips from people teaching me how to do things like that. Basically, we ask a question or basically shut up and hear what their answer is."

 

"I'm not really sure what creativity is. I'm not really sure if machines will ever be creative. A good experiment to try to prove that out is to try to get a machine to be as creative as possible and see where it falls flat."