Monica Rogati

Monica Rogati is a data scientist and advisor best known for distilling how companies should think about building data and AI capability. Born and educated in Romania, where she attended the Tudor Vianu National College of Computer Science, she moved to the United States for further study, earning a bachelor's degree in computer science from the University of New Mexico followed by a master's and PhD from Carnegie Mellon University, where her research focused on natural language processing and machine learning.

Rogati built her industry reputation as a senior data scientist at LinkedIn, where she worked on some of the recommendation and talent-matching systems that underpinned the company's growth, before becoming vice president of data at the wearables company Jawbone, where she led teams turning sensor data from fitness trackers into health and behavioural insights.

She is most widely cited for creating the "Data Science Hierarchy of Needs," a pyramid framework, echoing Maslow, that argues companies must get the unglamorous foundations right, reliable data collection, storage, cleaning and infrastructure, before they can meaningfully benefit from analytics, experimentation, and machine learning. The framework has become a standard reference used to explain to executives why AI initiatives fail without basic data infrastructure in place, and it has been reproduced across countless internal presentations, blog posts, and industry talks.

Since leaving full-time operating roles, Rogati has worked as an independent advisor and fractional chief data officer to numerous startups, and as an equity partner and scientific advisor to firms including Data Collective (DCVC) and CrowdFlower, helping investors and founders assess the technical and commercial viability of data and AI ventures. She has been named a Fortune "Big Data All-Star" and one of Fast Company's 100 Most Creative People in Business.

Rogati's influence stems less from a single company than from shaping how the industry talks about data maturity: her hierarchy of needs framework remains one of the most frequently invoked mental models for organisations trying to figure out where to focus their data investment, and she continues to advise startups and investors on separating substantive AI capability from hype.