Position: Principal Associate, Machine Learning

• Selected for the Discover integration effort, supporting migration of 33M existing accounts onto Capital One’s platform and reporting progress directly to leadership.
• Co-authored a Customer Management Risk Model whitepaper and delivered an anomaly-feature analysis workflow that flagged abnormal patterns in minutes (vs. prior manual investigation).
• Built tri-bureau feature datasets to enable historical scoring and low-volume model tests across multiple acquisition segments (Upmarket, Mainstreet, Student/No Credit), accelerating DS iteration cycles.
• Owned upgrades and maintenance of Kubeflow pipelines supporting 70M+ accounts for a transaction underwriting credit-risk model (Thunder), improving reliability and scalability.
• Optimized Apache Spark configurations to expand ETL backfill capacity from 1 day → 6 months per run across 3 years of retro-scoring to support a v2 market launch.
• Stabilized multiple pipeline stages (root-caused failure modes, improved retries/validation), saving ~10 hours/week for DS partners and improving run success rate.
• Supported in-market model operations (NPSL / “No Preset Spending Limit”) through Kubeflow + Spark performance optimizations.
• Served as first-line owner for daily underwriting ETL/pipeline failures—triaged incidents, restored runs, and partnered with platform/DS teams to unblock experiments quickly.
• Co-led Testing Education & Culture (Code Excellence Initiative): designed/piloted curriculum for pytest + property-based testing, increasing testing adoption across teams.
• Mentored 3 incoming employees through CODA, improving onboarding and early productivity.
• Recognized for rapid triage of unannounced upstream breaking changes, preventing significant production downtime.

Tech: Snowflake, Databricks, SQL, New Relic, Python, Jenkins, Kubeflow, Splunk, Apache Spark