Data science as a product parameter? with David Herberich at Branch
The Data Standard
Episode Summary
Although the term “data scientist” has a nebulous definition at best, there are a few characteristics that seem to unite us: we are curious, we like finding nuggets of information buried within a mass of data, we like solving hard problems, we are technically inclined. There are tons of fantastic programs offering master’s degrees in data science and analytics, and these programs teach statistics, data mining, machine learning. But what if you need to build an actual product that is used by people? Does knowing the mathematics of back propagation help you?
To our knowledge, most data scientists are not trained in product development. Yet, products that are underpinned by machine learning are becoming essential tools for many businesses, particularly in the retail and eCommerce space. Building data products require data scientists, but as a whole, we are…not really great at building them. How can we contribute our talents to this process without derailing it and making our product development leadership roll their eyes and cringe?
Meet The Host
Catherine Tao
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
David Herberich
VP, Head of Data at Branch
Data-based strategist who utilizes econometric modeling, experimental design and understanding of business objectives and constraints in order to properly evaluate existing and proposed strategies, recommending improvements and processes to achieve goals.
