Summary
Juliet Hougland is a Staff Software Engineer and data scientist with 13 years of experience building scalable experimentation, evaluation, and member-specific data systems at Netflix and other data-driven companies. She blends a strong applied mathematics background with hands-on machine learning and big data engineering, having led platform and experimentation redesigns that enable adaptive, sequential, and GenAI evaluation workflows. Juliet has a track record of shipping production-grade data infrastructure—from model deployment and lineage tracking to OCI/algo observability—and framing science methodologies into self-serve capabilities for researchers and DSE stakeholders. An active open-source contributor and frequent international keynote speaker, she has contributed to projects like Marquez, Apache Spark, Scalding, and Kiji, helping bridge research and production. Based in Seattle, she also advises startups on growing data science capabilities, bringing both strategic judgment and deep technical execution to product-critical problems. A less obvious strength is her history of translating complex statistical methods into operational metrics and tooling that drive engineering culture change and measurable product impact.
13 years of coding experience
12 years of employment as a software developer
Bachelor's Degree, Mathematics-Physics, Bachelor's Degree, Mathematics-Physics at Reed College
Master's Degree, Applied Mathematics, Master's Degree, Applied Mathematics at University of Colorado Boulder