Daniel W. Livingston: Enterprise Data Science & Platform Architecture
Definition-Driven Data Systems
Moving Beyond the Notebook
While many data science initiatives stop at an isolated local prototype, I architect cloud-native machine learning platforms designed for enterprise scale. I build definition-driven systems that bridge the gap between raw, high-velocity telemetry and governed, model-ready features.
With over a decade of experience across the financial and energy sectors, a Master’s in Applied Statistics, and the disciplined execution of a U.S. Army veteran, I design solutions that prioritize strict governance, fail-safe ingestion, and auditable single sources of truth.
The true value of machine learning isn't just algorithmic accuracy; it's operational resilience. I combine statistical rigor with robust platform engineering—specifically within the Snowflake ecosystem—to deliver:
Push-Down Compute: Keeping heavy processing where the data lives. By utilizing native warehouse capabilities and Snowpark, I ensure strict training-serving parity to eliminate the risk of offline-online feature drift.
Predictive Systems & Asset Risk: Specializing in survival analysis, run-to-failure modeling, and Piecewise Remaining Useful Life (RUL) forecasting to transform time-series telemetry into actionable condition-based maintenance and risk insights.
Fail-Safe Architecture: Designing modular, 5-schema frameworks (RAW, STAGING, CORE, MARTS, FEATURE) that enforce separation of concerns and maintain immutable ledgers for absolute compliance and auditability.
Current Focus I am actively engineering scalable predictive maintenance and risk platforms using cloud-native feature stores, coupled with robust MLOps experiment tracking. My focus is on designing infrastructure where the exact pipeline used to train historical risk models is identically matched to live production scoring, ensuring solutions are not just highly accurate, but fully governed and safe for regulated industries.
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