Incentives, Accountability, and the Data Leader’s Dilemma скачать в хорошем качестве

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. 00:00 Machine Learning vs Analytics in Business 04:50 Daragh's Journey from Academia to Industry 07:17 The Role of Machine Learning in Decision Making 18:34 Balancing Human Judgment and Machine Learning 23:17 Building Effective Human-Machine Workflows 32:12 Challenges in Emulating Workflows 33:01 Organizational Structures and Processes 35:05 Incentive Structures in Data Science 36:26 Human-Computer Symbiotic Systems 38:46 Career Incentives and Maintenance Challenges 41:49 Adapting to New Technologies 49:06 Structuring Data Science Teams 59:18 Driving Impact as Data Leaders 01:02:37 Conclusion and Final Thoughts