Several days ago I posted a number of pictures on Facebook of many of the amazing women who took the stage at SourceCon in Anaheim, California. One of those amazing women, who delivered the opening keynote, joins me for a special Big Picture Channel podcast on TotalPicture Radio.
In her role as Senior Director, Data Science for Randstad Sourceright, Summer Husband is responsible for helping to bring recruiting data to life. Through deep analysis and visualization, applying predictive analytics and machine learning, Summer is able to support clients' preemptive decision making and deliver continuous and measurable improvement across all aspects of talent acquisition. This ability places her organization at a clear competitive advantage.
Machine learning, predictive analytics, AI, Big Data is on every conference agenda this year.
Here's a real world example of why. A recent article in Business Insider showcases Amazon's new brick-and-mortar bookstore in Chicago, with the headline Inside Amazon's new Chicago store, where the books for sale have an average rating of 4.5 stars online. That's right, all of the books on display have at least a 4.5 customer rating. But it goes much, much deeper. Amazon knows what Chicagoan's like. Think about how Amazon is able to use its massive amount of data to make this one bookstore a reflection of the tastes, interests, and aspirations of the community it serves.
Before working in the recruiting space, Summer worked as an analyst with Metron, a scientific consulting firm tasked with solving complex national defense problems. During her tenure with Metron, she worked on submarine tracking, missile defense and airborne laser mine detection, for which she received a certificate of recognition from the U.S. Navy for her contributions. Prior to her data science work, Summer received a PhD in applied math from Rice University.
Self-driving cars, Amazon's personalized recommendations, survival statistics in medicine - these are all examples of the power of machine learning. The concept is not only making news and changing the way we live, it's having a real and growing impact on all aspects of business - and recruiting is no exception. What exactly is machine learning? How does it work? How can we put it to work in the recruiting space?
In our interview, Summer will explain the concepts and walk through examples of machine learning at work. Whether you are a strategic decision maker or work on the front lines of recruiting, this is a great chance to get a clear view into an area of innovation that will change sourcing, talent acquisition, recruiting and talent management in the future.
Summer, welcome to TotalPicture Radio.
In the category of pure luck, (no algorithms or machine learning needed), Summer sat next to me at lunch and I was able to talk her into talking with me on TotalPicture Radio. Summer, I'd like to start by having you tell us about your background, you told me that you grew up in a small town in Alabama...
Before joining Randstad Sourceright and working in the recruiting space, you worked as an analyst with Metron, a scientific consulting firm tasked with solving complex national defense problems. That sound pretty exciting. What attracted you to recruiting?
As a real, live, data scientist, how do you define machine learning and why has it become so important?
For those of us who are math challenged, can you explain how algorithms can help us?
Back to the topic of machine learning -- I'm assuming Moore's Law is in play here -- meaning the huge advancement in processing power is why all of this is now possible?
In my intro I talked about Amazon's new bookstore. At SourceCon you talked about a number of examples of machine learning -- Netflix, Airbnb, Tesla, Waymo, and others can you share some of the data you highlighted?
So we're going to return to Amazon for a minute, Summer, because as everyone with an Amazon Prime account knows, they're masters at this -- you showed a slide -- Input - (clicks & purchases); Algorithm - (Deep Learning); Output - (Recommendations). Can you step us through this and explain deep learning?
Another company you talked about is Stitch Fix - what makes them special?
One example of machine learning you used (which I think everyone can relate to) is the movie Titanic - specifically who would survive (cue the violin strings) - could you give us a quick overview of how you laid this out for the audience?
So let's take everything we've talked about and relate it to recruiting. You're able to assess at the time they're created, what reqs are high, medium, and low risk of remaining unfilled by the target fill date.
Just like using predictive analytics companies can tell who is in danger of leaving, or getting recruited away by a competitor. Am I right?
What do all of the advances in machine learning mean for recruiting as a profession over the next three to five years?
What would you like to share with our audience that we've not discussed?
How can our listeners connect with you?
About Peter Clayton
Peter Clayton, Producer/Host, is an award-winning producer/director of radio, television, documentary, video, interactive and Web-based media who has created breakthrough media for a wide array of Fortune 100 clients.