Jonathan Brownstein

Senior Data & Analytics Leader · Predictive Modeling & ML · Executive KPI Dashboards |

Senior data & analytics leader with 15+ years building enterprise-scale, audit-ready analytics in highly regulated industries. I translate complex datasets—including platforms processing 1T+ records—into KPIs, dashboards, predictive models, and executive narratives that drive operational decisions.

15+
Years Experience
1 Trillion+
Records Processed
4
Degrees & Certs
Jonathan Brownstein
Python Databricks Power BI SQL
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C-Suite Recognized Impact & leadership acknowledged at the executive level
1 Trillion+ Records Automation and analytics at massive enterprise scale
Audit-Ready Analytics Production-grade ML & reporting in regulated industries
7 Years at Hannover Re Deep domain expertise in reinsurance & actuarial data

About Jonathan Brownstein

I am a Senior Data & Analytics Leader specializing in regulated industries—building enterprise-scale, audit-ready analytics that turn complex datasets into the KPIs, dashboards, and executive narratives leadership actually decides on.

With 15+ years of experience, I design and operationalize predictive models, classification machine learning, and statistical outlier detection alongside Power BI, Tableau, and SSRS reporting platforms. I manage external vendors and consultants on scope, quality, and delivery, and partner across actuarial, finance, BI, compliance, and governance to land production-ready, results-driven solutions.

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

Vendor & Stakeholder Leadership

Manage external vendors and consultants on scope, quality, and delivery while partnering across actuarial, finance, BI, compliance, and governance

Professional Experience

Senior Data Analyst III

Enterprise Data Scientist & Analytics Architect (de facto)
Hannover Reinsurance Company LLC 2019 – Present

Operate as the de facto Data Scientist and Analytics Architect within the Chief Data Office of a heavily regulated reinsurance organization. Partner with actuarial, finance, BI, compliance, and governance leadership to design and operationalize production-grade analytics, predictive models, and reporting across enterprise platforms—serving as technical lead, architecture owner, and quality reviewer while managing external vendors and consultants on scope, performance, and delivery.

  • ARIMA Forecasting Platform — Designed and deployed an automated time-series forecasting system to predict Net Amount at Risk (NAR), enabling early anomaly detection and proactive actuarial decision-making that directly informed executive strategy.
  • ML Classification for SAL — Led design of classification-based machine learning solutions to modernize the Same-As-Link process, materially improving accuracy, consistency, and auditability of regulatory reporting. Recognized formally by the former CEO.
  • Statistical Outlier Detection — Built enterprise-grade frameworks using Probability Density Function (PDF) theory and advanced data mining techniques to identify large-scale attribute shifts, protecting data integrity and preventing downstream escalations.
  • Valuation Extract Comparison Architecture — Developed the organization's first valuation extract comparison architecture, enabling repeatable deep-dive analysis of valuation movements across time periods and data sources and accelerating finance review cycles.
  • Field Inventory Report — Architected a comprehensive metadata and data-lineage reference asset that became the authoritative enterprise reporting standard for the Strategic Data Warehouse (SDW).
  • Executive & Operational Dashboards — Designed and delivered Power BI, Tableau, and SSRS dashboards, visualizations, and reports that translate complex datasets into actionable Key Performance Indicators (KPIs) for executive and operational stakeholders.
  • Process Improvement — Drove data-driven process improvement initiatives that reduced manual review cycles and strengthened reporting accuracy across enterprise pipelines.
  • Vendor & Consultant Management — Manage external vendors and consultants supporting analytics, BI, and reporting initiatives—selecting partners, setting performance and quality expectations, and overseeing scope, timelines, and delivery against business goals.
  • Trusted Technical Partner & Audit-Ready Delivery — Serve as a trusted technical partner to actuarial, finance, and C-suite leadership, translating complex analytical findings into clear business narratives while partnering with data engineering, BI, compliance, and governance to ensure solutions are scalable, audit-ready, and compliant.

Assistant Business Controller

Chicago Pneumatic Company LLC 2012 – 2018
  • Vendor & Consultant Management — Managed external vendors and consultants on analytics, reporting, and systems initiatives—selecting partners, setting performance expectations, and overseeing delivery quality, scope, and timelines to keep outcomes aligned with business goals.
  • Cross-Functional Analytics Modernization — Led analytics and reporting modernization initiatives in partnership with HR, Sales, Marketing, and Executive Leadership teams, delivering data-driven insights that informed operational strategy.
  • Automated Reporting & KPI Dashboards — Designed automated reporting frameworks and KPI dashboards that improved data accessibility, consistency, and operational efficiency, establishing foundational data governance and quality assurance practices.
  • Stakeholder Translation — Served as the primary bridge between business stakeholders, vendors, and technical teams, translating complex data findings into clear, actionable business outcomes.

Architecture & Technical Expertise

Analytics & Data Science

  • Predictive modeling (ARIMA / time-series)
  • Classification machine learning
  • Statistical outlier & anomaly detection
  • Data mining at enterprise scale
  • Applied econometrics & statistical modeling

Visualization & Reporting

  • Power BI, Tableau, and SSRS dashboard design
  • KPI tracking & performance metrics
  • Executive & operational reporting
  • Insight storytelling for C-suite audiences

Data & AI Architecture / Governance

  • Lakehouse and cloud data warehouse design
  • Metadata management & data lineage
  • Audit-ready, reusable enterprise data assets
  • ML deployment, monitoring & lifecycle governance

Platforms & Tools

Python R SQL VBA Databricks Power BI Tableau SSRS Cloud DW Relational DBs VS Code Cursor AI

Leadership & Delivery

  • External vendor & consultant management
  • Cross-functional collaboration with actuarial, finance, BI, compliance & governance
  • Regulated-industry analytics delivery
  • Process improvement & operational efficiency

Featured Projects & Research

Master's Thesis

Research
Nominated for Outstanding Master's Thesis Award

"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.

University of North Carolina at Charlotte 2020
Econometrics Time Series Regression R Python

ARIMA Forecasting Platform

Enterprise ML

Architected 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.

Hannover Reinsurance
ARIMA Python Time Series Anomaly Detection SQL

ML Classification — SAL Modernization

Data Governance
Received formal recognition from the CEO

Designed 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.

Hannover Reinsurance
Classification Python SQL Data Governance Databricks

Statistical Outlier Detection Framework

Advanced Analytics

Built 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.

Hannover Reinsurance
PDF Theory Python Statistical Modeling SQL Databricks

Valuation Extract Comparison Architecture

Advanced Analytics

Developed the organization's first valuation extract comparison architecture, enabling repeatable deep-dive analysis of valuation movements across time periods and data sources. The platform replaced ad-hoc spreadsheet reconciliations with a scalable, audit-ready review process that accelerated finance team review cycles.

Hannover Reinsurance
SQL Python Valuation Analytics Data Modeling Databricks

Education & Credentials

MS, Economics

University of North Carolina at Charlotte

Certificate, Applied Econometrics

University of North Carolina at Charlotte

BS, Economics

University of North Carolina at Charlotte

FLMI

Fellow, Life Management Institute LOMA

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