Yelp Head of Data Science will discuss Product Management for Data Science on Jan. 22 in SF
Yelp’s Head of Data Science will be speaking in San Francisco on January 22, discussing Product Management for Data Science.
The speaker, Justin Norman, says that the talk will introduce and explore Data Science Products, including what they are and why we should care about them.
“We’ll dive into the Data Science Product Manager. It’s a role that includes everything a traditional PM does. It also requires an operational understanding of advanced analytics and machine learning software development. Plus, a realistic view of its responsibilities, key skills, capabilities, and limitations,” says Norman. “The talk is aimed at anyone who is interested in the intersection of Data Science and Product leadership.”
His talk will take place on Jan. 22 from 7-9 p.m. at Yelp Headquarters. Tickets range from $20 to $25 and can be purchased online.
Justin serves as Head of Data Science at Yelp. He is a career data professional and Data Science leader with experience in multiple industries and companies. Previously, Justin was the Director of Research and Data Science at Cloudera Fast Forward Labs, head of Applied Machine Learning at Fitbit, the head of Cisco’s Enterprise Data Science Office and a Big Data Systems Engineer with Booz Allen Hamilton.
In another life, Justin served as a Marine Corps Officer, with a focus on Systems Analytics and Device Intelligence. Justin is a graduate of the US Naval Academy with a degree in Computer Science and the University of Southern California with a Master’s Degree in Business Administration and Business Analytics.
Ahead of his talk, Norman answered a few questions about his product management and data science experience.
What is Your Favorite Product and Why?
It probably shouldn’t surprise anyone that the data scientist’s favorite product is one that you can experiment and tinker around with. He not only appreciates the product but the possibilities it represents.
“The Raspberry Pi. I’m really excited by an open standard, decentralized platform-style products. Rather than build a small computer and deliver it in a form factor that appeals to the current largest TAM, the developers of Raspberry Pi provide a general-purpose product and challenge the community to build on top of it. Sometimes, in VERY unexpected ways. Additionally, the entry-level price point normalizes the user population and really opens up the adoption potential. The sales are good (over $19 million), but the impact and potential are great.”
What Makes a Great Product?
For Norman, it’s ultimately all about usefulness. However, run that usefulness through the filter of being a data scientist.
“An awesome Data Science product is one that utilizes a Data Science capability, combined with one or more data products, to make or augment decision-making in a way that is useful.”
What Makes a Great Product Manager?
We’ve heard many a great product manager speak on the importance of working with other teams and wearing many hats. Norman, as Head of Data Science at Yelp, sees how important it is for him to work hand in hand with Product Management.
“As a data scientist, oftentimes it’s easy to forget the big picture while optimizing a particular model or solving a gnarly feature engineering challenge. At the end of the day, Data Science/Machine Learning/AI artifacts are actually products themselves (with consumer requirements, resource needs, and usability considerations), in addition to providing capabilities to other products. The most successful teams that I have been a part of have very tight alignment between Data Science and Product Management. In fact, I’m so passionate about this topic that I’m currently writing a book on AI Product Management.”
Speaking of books, Norman recommended a few to our readers. He suggests checking out Monetizing Innovation: How Smart Companies Design the Product Around the Price. Other books to pick up include Data Science for Business, and Think Stats.
If short reads on your computer, laptop or smartphone are more your speed, Norman also has a few suggestions. He says to check out the Towards Data Science Blog. You can also look into Fast.AI, or “Machine Learning for Product Managers” by Neal Lathia. His last suggestions is “Everything We Wish We’d Known About Building Data Products” from DJ Patil.