Welcome to Episode 37 of the Talent Intelligence Collective podcast! In this data-driven episode, Alan Walker and Toby Culshaw from Lightcast (with Alison taking a well-earned break) delve into the world of talent analytics with Steve Kerwin, who leads the data and analytics organisation for Amazon's Stores Talent Acquisition team.

The news roundup kicks off with a critical examination of Trump's manufacturing revival plans against the sobering reality of skills shortages across the US, with Deloitte projecting 1.9 million unfilled manufacturing jobs by 2033. The conversation shifts to Shopify's CEO's controversial policy of refusing to approve new headcount unless teams can demonstrate why AI can't perform the work, sparking a thoughtful discussion about the practical limitations of AI implementation. The segment concludes with a cautionary tale of an AI shopping app founder charged with defrauding investors after allegedly using human contractors in the Philippines instead of the advertised AI technology.

Steve shares his eight-year journey building Amazon's talent acquisition analytics capabilities from the ground up, offering candid insights into scaling data solutions to thousands of users across one of the world's largest employers. He emphasises the critical importance of candidate experience data and how qualitative feedback, when properly captured and analysed, can transform hiring success at scale.

The discussion explores the unique challenges of leading a diverse team of BI engineers, software developers, and data engineers, with Steve advocating for a servant leadership approach that empowers team members to grow their skills and tackle increasingly complex problems. He provides a refreshingly honest assessment of the persistent disconnect between talent acquisition, HR, and finance when it comes to operational forecasting and demand planning, highlighting the need for better integration across these traditionally siloed functions.

For organisations just beginning to build their analytics capabilities, Steve offers practical advice, emphasising the importance of establishing solid foundational data structures before attempting more sophisticated analyses. He compares building analytics capabilities to constructing a house: "I can't build you bay windows and roofs if I don't have the basement laid."

Until next time, stay curious, stay analytical, and most importantly, stay intelligent!

As ever - big thanks to our sponsors: https://lightcast.io