Jonathan Brownstein
Enterprise Data Scientist & Data/AI Architect |
Data, AI, and Analytics leader with 15+ years designing, modernizing, and operationalizing enterprise-scale data, ML, and analytics ecosystems. Trusted technical partner to actuarial, business, and executive leadership with C-suite–recognized impact and automation at massive scale.
About Jonathan Brownstein
I am an Enterprise Data Scientist & Data/AI Architect specializing in insurance and financial services—designing cloud-native, end-to-end data ecosystems that transform how organizations make decisions.
With 15+ years of experience, I architect large-scale data warehouse modernizations, production-grade machine learning systems, and advanced analytics solutions. I operate at the intersection of data engineering, ML architecture, and business strategy—serving as a trusted technical partner to actuarial, business, and executive leadership.
My work has earned C-suite recognition, driven data democratization initiatives, and delivered automation at massive scale across 1T+ records.
Data & AI Architecture
Enterprise-scale lakehouse, cloud DW, and metadata governance solutions
Machine Learning
Production ML systems—from ARIMA forecasting to classification and anomaly detection
Insurance & Finance
Deep domain expertise in reinsurance, actuarial analytics, and regulatory data
Professional Experience
Senior Data Analyst III
Enterprise Data Scientist & ML Architecture FocusOperate as a de facto Data Scientist and Data & ML Architect within the Chief Data Office ecosystem, designing scalable, production-grade analytics and machine learning solutions across actuarial, finance, and enterprise data platforms.
- ARIMA Forecasting Platform — Architected an automated forecasting system to predict Net Amount at Risk (NAR), enabling early anomaly detection and proactive actuarial decision-making.
- ML Classification for SAL — Designed and operationalized classification-based ML solutions to modernize the Same-As-Link process, materially improving accuracy, consistency, and data governance. Received formal recognition from the former CEO.
- Statistical Outlier Detection — Built enterprise-grade frameworks leveraging Probability Density Function theory to identify large-scale attribute shifts and prevent downstream actuarial escalations.
- Enterprise Data Assets — Led the design of the Field Inventory Report, a comprehensive metadata and data-lineage architecture that became the authoritative reference for the enterprise SDW.
- Valuation Comparison Architecture — Developed the organization's first valuation extract comparison system, enabling scalable deep-dive analysis of valuation movements across time and data sources.
- Technical Leadership — Serve as architecture owner, partnering with data engineering, BI, actuarial, and governance teams to align ML solutions with enterprise data strategy.
Assistant Business Controller
- Led cross-functional analytics and reporting modernization initiatives, partnering with HR, Sales, Marketing, and Executive Leadership.
- Designed automated reporting frameworks that improved data accessibility, consistency, and operational efficiency.
- Served as a bridge between business stakeholders and technical teams, establishing the foundation for later enterprise data and architecture leadership.
Architecture & Technical Expertise
Data & AI Architecture
- Enterprise Data & AI solution blueprints
- Lakehouse and cloud data warehouse design
- Metadata management, data lineage, and governance
- Reusable, enterprise-grade analytical data assets
Data Engineering & Processing
- High-volume structured and semi-structured data ingestion
- Batch and near-real-time analytics pipelines
- ELT and transform-heavy analytical architectures
- Performance optimization for large-scale data processing
Machine Learning & Advanced Analytics
- Time series forecasting (ARIMA)
- Classification models
- Statistical modeling and applied econometrics
- Outlier and anomaly detection
- Model deployment, monitoring, and lifecycle governance
Platforms & Tools
Industry Focus
- Insurance and reinsurance analytics
- Financial risk and valuation
- Actuarial and regulatory data environments
Featured Projects & Research
Master's Thesis
Research"Has the Shrimping Industry Bounced Back Since Deepwater Horizon?" — A comprehensive econometric analysis examining the economic recovery of the U.S. shrimping industry following the 2010 Deepwater Horizon oil spill. Employs time series analysis, regression methods, and advanced statistical modeling to assess long-term economic impacts.
ARIMA Forecasting Platform
Enterprise MLArchitected an automated time-series forecasting system to predict Net Amount at Risk (NAR) across the enterprise reinsurance portfolio. The platform enables early anomaly detection and proactive actuarial decision-making, replacing manual review processes with scalable, repeatable analytics.
ML Classification — SAL Modernization
Data GovernanceDesigned and operationalized classification-based machine learning solutions to modernize the Same-As-Link (SAL) process, materially improving matching accuracy, consistency, and data governance across the enterprise data warehouse.
Statistical Outlier Detection Framework
Advanced AnalyticsBuilt enterprise-grade statistical outlier detection frameworks leveraging Probability Density Function (PDF) theory to identify large-scale attribute shifts across the data warehouse. The system prevents downstream actuarial escalations by catching anomalies before they propagate through reporting pipelines.