Incentives, Accountability, and the Data Leader’s Dilemma

Daragh Sibley, Chief Algorithms Officer at Literati and former Director of Data Science at Stitch Fix, joins High Signal to unpack how machine-learning moves from slide-deck promise to bottom-line impact. He walks through his shift from academic research on how kids learn to read to owning inventory and personalization algorithms that decide which five books land in every child’s box. We dig into the moment a data leader stops advising and starts owning P&L-critical calls, why some problems deserve simple analytics while others need high-dimensional models, and how to design workflows where human judgment and algorithmic predictions share accountability. Along the way we talk incentive design, balancing exploration and exploitation in inventory, and measuring success in dollars—not dashboards.

LINKS

• Daragh on LinkedIn
• Eric Colson on Why 90% of Data Science Fails—And How to Fix It
• Sudarshan Seshadri on High-Stakes AI Systems and the Cost of Getting It Wrong
• Delphina's Newsletter