Summary
Dustin Lennon is a seasoned software engineer and applied statistician with 17 years of experience building production-grade data systems and algorithms, especially around large-scale data processing with Databricks and Apache Spark using Scala and Python. He blends deep numerical and statistical expertise—from C++ numerical code and multithreaded optimization to adaptive Bayesian models—with pragmatic product-focused delivery across companies like Conviva, ServiceNow, Microsoft, and Zillow. Dustin excels at socializing data-science best practices, driving data-aware design conversations, and turning underperforming algorithms into interpretable, actionable results. He’s comfortable across the stack and tooling—SQL, bash, SBT, GCP—and still values fundamentals, routinely probing candidates on topics from endianness and data skew to the central limit theorem. His background includes building production time-series and anomaly-detection systems, portfolio optimization for lending, and stabilizing complex estimation pipelines, reflecting an unusual mix of theoretical depth and production rigor. Based in Seattle, he also codes full-stack and Discord bots in side projects, signaling curiosity beyond core data engineering.
16 years of coding experience
12 years of employment as a software developer
Master’s Degree, Applied Mathematics, Master’s Degree, Applied Mathematics at University of Washington
BSE, Computer Science / Applied Math, BSE, Computer Science / Applied Math at Princeton University