
October 02, 2026
How We Accelerate ML Deployment by Empowering Data Scientists (Part II)
Explores how empowering data scientists with the right tools, infrastructure, and ownership can streamline machine learning deployment, reduce bottlenecks, and accelerate the transition from research to production. Read more

September 11, 2026
How Root Rebuilt Its Telematics Platform for Scale
Learn how Root rebuilt its telematics platform to handle growing scale, improve reliability, and create a more flexible foundation for processing the driving data that powers its insurance products. Read more

August 20, 2026
Why Great Talent Stays: A Survey of Root Quantitative Science’s EVP
Root’s Quantitative Science team shares what keeps great technical talent engaged, from meaningful work and talented colleagues to a culture built on collaboration, empowerment, and growth. Read more

August 04, 2026
How We Accelerate ML Deployment by Empowering Data Scientists (Part I)
Explore how Root empowers data scientists to move machine learning projects from experimentation to production faster with reproducible development environments, standardized tooling, and infrastructure designed to make best practices the easiest path. Read more

June 26, 2026
The Opposite of “Fake It Till You Make It"
Challenges the "fake it till you make it" mindset by showing how curiosity, humility, and a willingness to learn can lead to stronger collaboration, better decisions, and long-term success. Read more

December 15, 2021
Six Under-Appreciated Principles of Data Warehousing, Through the Eyes of a Data Scientist
Challenges can emerge between data science research and production. This article explores practical strategies for building more reliable, scalable machine learning pipelines. Read more

October 27, 2021
Types of distracted driving
Learn about the dangers of distracted driving and how much money you could save by staying focused on the road. Read more

September 08, 2021
The Data Science Research-Production Chasm
Explores the challenges that can emerge between data science research and production and practical strategies for building more reliable, scalable machine learning pipelines. Read more