Summary
Rob Schoenbeck is a Staff Machine Learning Engineer at Kiva with 11 years of experience building end-to-end data and ML systems that power mission-critical lending products. He’s driven measurable impact—shipping the first ML recommendation system that boosted conversion 16%, deploying LLM-powered features that added 3% conversion, and contributing $5–10M in incremental annual lending through search and ranking improvements. Rob owns the full ML stack from models and pipelines to Kotlin microservices and GenAI middleware, operating high-throughput systems (Elasticsearch at ~205K queries/day, $40M+/year disbursed) across hybrid AWS/GCP environments. He led major Airflow migrations and MLOps tooling work to keep 30+ pipelines running >99.5% successfully, and he teaches AI adoption internally to 100+ colleagues. With a PhD in Digital Humanities and a background in teaching, he excels at translating technical tradeoffs for non-technical stakeholders and designing data products that respect privacy and impact measurement. Colleagues rely on him where one engineer must own multiple layers—from research and modeling to production architecture and product strategy.
11 years of coding experience
9 years of employment as a software developer
Certificate, Data Science, Certificate, Data Science at University of California, Irvine Division of Continuing Education
PhD, Digital Humanities (English), PhD, Digital Humanities (English) at University of California, Irvine
English