Enterprise Data Strategy · Governed Analytics · Model & Data Validation · Executive Advisory

Lexington, South Carolina · LinkedIn · GitHub

No Cold Handoffs: Logic, controls, validation, evidence, and interpretation travel together.

Senior analytics and applied data science leader with 16+ years of experience across Wells Fargo, the Office of the Comptroller of the Currency, and the U.S. Census Bureau.

I combine hands-on SAS, SQL, Python, PostgreSQL, longitudinal consumer-credit analytics, and applied data science with enterprise data strategy, governed decision systems, model and data validation, and executive advisory leadership. My work connects business and regulatory requirements to source-data lineage, analytical methodology, decision logic, implementation controls, monitoring, exception evidence, and controlled operational follow-through.

At Wells Fargo, I was promoted from Vice President to Executive Director while serving as principal analytical architect for Consumer Auto programs exceeding $1B in exposure and customer impact. My experience includes CPI analytical-system governance from 2018–2025, leadership across 15+ additional remediations, and enterprise controls spanning 500+ submissions and 100M+ account-month furnishing actions.

At the OCC, I supported 16 economists and examination teams with longitudinal consumer-credit data, survival and logistic modeling, life-of-loan PD and CECL analysis, credit benchmarking, alternative-data engineering, and published research support. Earlier, I modernized federal statistical production and validation at the U.S. Census Bureau.

My public portfolio demonstrates how those disciplines translate into merchant-financing strategy, enterprise credit decisioning, survival modeling, forensic data quality, reconciliation, regulatory remediation, model and data validation, Power BI release assurance, and executive-ready analytical evidence.

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I integrate fragmented data, independently managed processes, and cross-functional requirements into governed analytical environments that support consistent execution and reliable executive decisions.

I design transparent rule, policy, strategy, and treatment frameworks with configurable parameters, account-level outcomes, reason codes, exception states, monitoring, archives, and certified consumption boundaries.

I develop interpretable frameworks connecting segmentation, survival and hazard modeling, scenario design, cross-validation, calibration, model diagnostics, sensitivity analysis, and stakeholder evidence.

I apply independent recalculation, benchmark and challenger analysis, source-to-target reconciliation, matched-population comparison, UAT, sensitivity testing, model diagnostics, exception management, and effective challenge.

I translate complex analytical and operational evidence into concise risk/fact/options narratives covering the issue, dependencies, customer and control implications, recommendations, required decisions, and follow-through.

I connect requirements, methodology, population logic, code, validation evidence, execution artifacts, decision records, controls, ownership, and implementation across independently accountable teams.

View the repository · Review the Module 2 / G3 tag · Open Releases

Open the architecture full size · Architecture PDF · Ten-page executive strategy brief · Module 1 / G2 lineage · Module 2 / G3 lineage

Current governed position: Module 1 / G2 accepted · Module 2 / G3 accepted · G2_M1_CONTRACT = PASS · G3_M2_CONTRACT = PASS · Campaign Scale Certification current

A deterministic, synthetic PostgreSQL 15 enterprise platform for merchant sales-based financing tied to daily point-of-sale activity and sales-linked repayment.

The platform begins before application—with acquisition sources, campaigns, touchpoints, attribution, and merchant-acquisition cost—and extends through merchant operating evidence, cash-flow capacity, resilience, risk, comparative loss, unit economics, eligibility, pricing, counteroffers, final-offer authorization, simulated activation and operating states, servicing, reconciliation, portfolio analytics, strategy comparison, optimization, and enterprise G3 certification.

The build is designed as an integrated decision system rather than a collection of disconnected scripts. Requirements, parameters, source lineage, business logic, controls, validation, evidence, interpretation, and ownership remain connected at every boundary.

Launch With Discipline Capability advances only after the preceding boundary is validated, evidenced, and contract-certified.	From Decision to Enterprise Certification Module 2 converts accepted G2 evidence into governed offers, simulated operations, portfolio strategy, and an accepted G3 consumption boundary.
Proof Before Scale Every stage must generate, validate, challenge, reconcile, and certify before the enterprise boundary advances.	The Opportunity Ahead The next governed proof expands accepted-fidelity replay through performance shakedown and full-campaign certification.

Open the complete ten-page brief · View the ten-page contact sheet

Acquisition Source, Campaign & Attribution
→ Application & Requested Structure
→ POS, Settlement, Deposit & Liquidity Evidence
→ Cash-Flow, Capacity & Operating Resilience
→ Integrated Risk, Exposure, Recovery & Comparative Loss
→ Unit Economics & Merchant Acquisition Cost
→ G2 Certified Consumption
→ Eligibility & Policy Gates
→ Pricing, Structure & Counteroffers
→ Final-Offer Authorization
→ Simulated Booking, Funding & Activation
→ Remittance, Exposure & Portfolio Monitoring
→ Early Warning, Servicing & Intervention
→ Payment Reconciliation & Account-State Certification
→ Portfolio KPI & Servicing Analytics
→ Strategy Comparison, Simulation & Optimization
→ G3 Enterprise Portfolio Certification
→ Campaign Scale Certification

• deterministic merchant, owner, processor, application, POS, settlement, deposit, and liquidity foundations;
• acquisition-source taxonomy, campaign funnels, application touchpoints, deterministic attribution, and merchant acquisition cost;
• source confidence, verification evidence, fraud indicators, and processor continuity;
• matched baseline and adverse scenarios using the same governed identities.

• eligibility and policy gates;
• pricing, terms, factor-rate, and remittance design;
• configurable counteroffers and alternative structures;
• final-offer authorization;
• transparent route, disposition, reason-code, and rationale evidence.

• deterministic simulated booking, funding, activation, and initial-limit states;
• simulated daily remittance, exposure, and performance monitoring;
• early-warning indicators and intervention triggers;
• servicing, restructures, collections, and lifecycle controls;
• payment reconciliation and certified account-state evidence.

• portfolio KPI and servicing analytics;
• matched baseline and challenger strategy comparison;
• strategy simulation across governed scenarios and objectives;
• portfolio optimization and explicit trade-off analysis;
• certified G3 enterprise consumption;
• controlled preparation for the next governed campaign cycle.

750 accepted-fidelity replay
→ 2,500 performance shakedown
→ 25,000 full campaign

Each step requires a separate fail-closed readiness and authorization gate. Preparation does not imply production fitness, causal inference, autonomous decisioning, or Module 3 authorization.

Explore the Module 2 stage tree · Review M2.12 enterprise certification · Open campaign-scale documentation · Open the artifact map · Review project history

Interpretation boundary: The platform uses deterministic synthetic data. It is not a production lending or servicing system, deployed credit policy, calibrated causal model, or autonomous decision service. Power BI references describe governed, Power BI-ready views and publication visuals—not a production dashboard deployment.

View the repository

A governed two-module decisioning environment with a PostgreSQL simulation core, independent SAS reconciliation evidence, and an interactive Power BI release-validation layer.

The platform demonstrates:

• deterministic application and risk generation;
• product-by-score risk surfaces;
• configurable credit-policy and treatment strategies;
• affordability and exposure controls;
• approvals, counteroffers, manual reviews, and declines;
• reason-code and decision-path traceability;
• matched baseline and challenger comparison;
• Expected Loss, approval, affordability, and exposure trade-offs;
• archive-backed campaign evidence;
• strategy-frontier analysis;
• Power BI executive-to-application reconciliation.

Governed scale:

View the Release Impact Explorer · Download the Power BI report

View the repository

A governed Python time-to-event analytical system that separates descriptive persona discovery from regularized Cox proportional hazards modeling and carries the work through risk stratification, cross-validation, calibration, proportional-hazards review, same-cohort scenario simulation, dependency reconstruction, stakeholder reporting, and immutable run evidence.

Synthetic Time-to-Event Data
→ Input Contract Validation
→ K-Means Persona Discovery
→ Regularized CoxPH Model
→ Five-Fold Cross-Validation
→ Out-of-Fold Calibration
→ Proportional-Hazards Review
→ Population Risk Stratification
→ Top-Quartile Target Cohort
→ Control / Improvement / Stress Scenarios
→ Dependency-Safe Re-Scoring
→ Predicted Survival Comparison
→ Executive PPTX + Technical PDF
→ Run Registry and Acceptance Evidence

Validated demonstration:

• 7,500 synthetic records;
• 2,964 observed events and 4,536 right-censored records;
• 12 governed CoxPH features;
• 0.827 five-fold mean concordance;
• 3 stable descriptive personas;
• 1,875 governed target-cohort records;
• 6 controlled scenarios;
• PASS_WITH_REVIEW acceptance posture with documented PH sensitivity.

The objective is not merely to fit a survival model. It is to build a governed analytical framework in which the model, validation, scenario assumptions, evidence, and interpretation remain connected.

View the repository

Python pre-model diagnostic engine that detects hidden and disguised nulls, dominant-value concentration, zero-variance fields, format and type defects, inconsistent categories, and anomalous distributions. Produces audit ledgers, executive scorecards, severity-ranked exceptions, and validation-ready handoff evidence.

View the repository

SAS metadata-driven A/B validation framework supporting schema drift, key-only and value-level differences, absolute and relative tolerances, date/time tolerances, character normalization, duplicate-key controls, severity-ranked exceptions, run metadata, UAT assertions, and controlled evidence exports.

View the repository

PostgreSQL longitudinal credit-reporting environment with a five-million-account baseline portfolio, month-level truth tables, financial and credit-impact windows, cure and manual-review logic, treatment assignment, delta-based remediation, before/after validation, and explainable reason-code evidence.

View the repository

SAS interval-reconciliation engine comparing proof-of-coverage evidence with lender-placed CPI policy windows. Demonstrates overlapping-interval consolidation, coverage validation, configurable lapse thresholds, policy-adjustment factors, parameter governance, and account-level customer-impact evidence.

View the repository

Memory-efficient SAS financial recalculation engine using one-year Constant Maturity Treasury rates, PROC FCMP, in-memory arrays, chunked account processing, annual compounding, final remainder-interest calculations, and account-level liability evidence.

This GitHub portfolio is a public proof layer for enterprise data strategy, governed analytics, applied data science, model and data validation, and decision-system architecture.

The repositories are not isolated notebooks or dashboard-only demonstrations. Each independently developed system pairs code with some combination of:

• enterprise architecture and code-flow documentation;
• business, analytical, and data requirements;
• controlled parameters and strategy configurations;
• deterministic synthetic or anonymized demonstration data;
• BRDs, validation summaries, and QA evidence;
• reconciliation and exception outputs;
• UAT scripts and acceptance checks;
• reason-code and decision-path evidence;
• run registries, immutable archives, and consumption contracts;
• acquisition, attribution, merchant-CAC, and unit-economics evidence;
• executive summaries, presentations, and reviewer paths;
• Power BI release validation and application-level traceability;
• technical runbooks and operating guidance.

The repository organization reflects the documentation, validation, version-control, and operating-model discipline I applied in regulated financial-services and federal environments.

Business Context and Decision Requirements
→ Data Fitness, Lineage, and Reconciliation
→ Model, Policy, Strategy, or Scenario Development
→ Scenario, Sensitivity, and Challenger Testing
→ Decision or Treatment Execution
→ Risk, Financial, Customer, or Lifecycle Impact Quantification
→ Validation, Monitoring, and Exception Management
→ Executive Narrative and Governed Follow-Through

The recurring architecture is:

Governed Inputs
→ Transparent Logic
→ Configurable Parameters
→ Account-Level Outcomes
→ Reason Codes and Exceptions
→ Validation Evidence
→ Executive Interpretation
→ Reproducible Handoff

The portfolio architecture reflects disciplines developed across three highly regulated public and financial-sector environments.

Wells Fargo | 03/2018–04/2026

• Executive Director — Senior Lead Analytics Consultant | 06/2022–04/2026
• Vice President — Lead Analytics Consultant | 03/2018–06/2022

Promoted while serving as principal analytical architect for Consumer Auto programs exceeding $1B in exposure and customer impact.

Led analytical workstreams across 15+ remediations, including SCRA, COVID-deferrals, disaster relief, a major data-center outage, inaccurate payoff-date reporting, CPI, and multiple credit-furnishing matters.

Owned the analytical components of the Remediation Target Operating Model, including the Data Analytical Approach, Population Identification Workbook, SAS/SQL code, population outputs, run procedures, validation evidence, Population Identification Review responses, Decision Forum analysis, and specialized review dependencies.

Authored, reviewed, edited, and presented Decision Forum materials using risk/fact/options narratives. Supplied underlying analytics, trade-offs, customer and control implications, and recommendations; aligned Legal, Compliance, Risk, Technology, Credit Bureau Management, Execution, IT&V, Audit, regulators, and senior leadership; and required missing decisions or changes to be formally documented before execution.

From 2018 through 2025, designed, operated, and governed Direct and Indirect Auto recalculation tools, proof-of-insurance intake and QA, customer redress, time-value-of-money loss-of-use relief, prior-payment netting, manual-review integration, credit-reporting redress, tax reporting, recurring production, executive reporting, and Audit/regulatory support.

Independent Audit found 100% alignment between retained proof-of-insurance evidence and the reviewed third-party data.

Led CPI credit-reporting redress for approximately 850,000 accounts through approximately 200 external mass-maintenance submissions, with corresponding internal system-of-record updates and account/borrower-level before-and-after lineage across key Metro 2 fields.

• Consolidated 500+ multi-line-of-business submissions representing more than 100M account-month furnishing actions.
• Built a SAS-to-Teradata pipeline to standardize and validate line-of-business data and publish governed datasets.
• Established account-month SQL controls aligning remediation and furnishing history to prevent favorable-state overwrites.
• Led UAT across data mappings, decision rules, outputs, and exceptions.
• Evaluated 20M+ historical Consumer Auto Finance accounts through the control framework.
• Defended a unified correction that accelerated customer relief while preserving accountability for remediation- and furnishing-caused impacts.
• Designed targeted account- and impact-month re-furnishing logic that preserved favorable customer reporting and saved approximately $60,000 per submission.

Beyond the 15+ remediations led, served as an enterprise furnishing SME, peer reviewer, and mentor on 10+ additional credit-reporting remediations.

Built or supported reusable Common Code, Standard Data Format transformation, Teradata-based archival delivery, tax-reporting standardization, BRDs, Job Aids, operating procedures, department-wide training, knowledge continuity, and aged IT&V review resolution.

Research Analyst, Credit Risk Analysis Division | 03/2014–03/2018

Supported 16 economists and examination teams with model-ready data, analytical methods, validation logic, benchmarks, and decision evidence.

Selected experience:

• developed and tested Cox proportional hazards, discrete-time survival, binary/multinomial logistic, and OLS frameworks for delinquency, default, prepayment, and pricing;
• constructed and validated longitudinal bureau panels spanning 875,516 loans and 2,363,261 loan-year observations;
• assessed right-censoring, term-age interactions, prime/subprime segments, macro sensitivity, lifetime risk, and pricing;
• independently recalculated outputs, tested sensitivities, reconciled source data, and translated results into Tableau views;
• led review of third-party PD vendor contracts and data fitness for CECL and life-of-loan PD analysis;
• built syndicated-credit entity-resolution and fuzzy-matching logic for cross-bank internal risk-rating comparison;
• developed obligor-level sequencing for first and subsequent delinquency/default events across products;
• built ArcGIS branch-distance measures, a one-second Python market-event collector, and a 10,000+ document conversion and SAS ingestion pipeline;
• provided technical direction and mentoring to junior analysts and interns.

The authors of both the peer-reviewed and OCC long-term auto-loan studies publicly acknowledged me for “excellent research support.”

Selected research supported:

• Risks of Long-Term Auto Loans;
• A Puzzle in the Relation Between Risk and Pricing of Long-Term Auto Loans.

Survey Statistician | 07/2009–03/2014

Modernized national survey systems supporting the Annual Capital Expenditures Survey and Annual Retail Trade Survey.

Selected experience:

• led day-to-day production for a six-analyst nightly refresh operation and served as escalation point for processing failures and unusual conditions;
• architected a SAS-to-Access-to-JavaScript/HTML production pipeline;
• designed analyst views prioritizing major year-over-year discrepancies and high-impact nonrespondents;
• automated executive-ready industry narratives, completion summaries, and management reporting;
• built SAS-to-Excel/VBA validation workbooks for analyst-controlled review;
• developed editing, imputation, response-tracking, outlier, disclosure-avoidance, and benchmarking controls;
• maintained methodology, runbooks, troubleshooting guidance, management summaries, and published economic narratives.

SAS Enterprise Guide · SAS Macro Programming · PROC SQL · PROC FCMP · SQL · Teradata · PostgreSQL · DBeaver · Python · Git/GitHub · Tableau · ArcGIS · Excel/VBA · Microsoft Access · JavaScript/HTML · PuTTY Batch Processing · SharePoint · Jira · PowerPoint

PostgreSQL · Power BI Desktop · Power Query · DAX · Power BI Semantic Modeling · Interactive Report Design · pandas · NumPy · scikit-learn · lifelines · matplotlib · openpyxl · python-pptx · ReportLab · Relational Data Modeling · Archive and Evidence Tables

Cox Proportional Hazards · Kaplan-Meier · Discrete-Time Survival and Hazard Models · Binary and Multinomial Logistic Regression · Ordinary Least Squares · K-Means · Probability of Default · Loss Given Default · Expected Loss · CECL · Life-of-Loan PD · Cross-Validation · Out-of-Fold Calibration · Proportional-Hazards Diagnostics · Scenario and Sensitivity Analysis · Policy-Rule Simulation · Pricing Strategy · Counteroffer Governance · Champion/Challenger Comparison · Matched Strategy Comparison · Portfolio Strategy Simulation · Portfolio Optimization · Strategy-Frontier Analysis

Independent Recalculation · Benchmark and Challenger Analysis · Matched-Population Comparison · Source-to-Target Reconciliation · Data Quality and Lineage Review · UAT and Acceptance Testing · Reasonableness and Sensitivity Testing · Model Diagnostics · Calibration Review · Dependency Synchronization · Monitoring and Exception Management · Contract-Certified Consumption · Documentation Standards · Effective Challenge · Executive Decision Evidence

Data Standardization · Cross-LOB Integration · Requirements Traceability · Data Analytical Approaches · Population Identification Workbooks · Run Procedures · Governance Checkpoints · Decision Forums · Executive Narratives · Escalation Management · Control Reviews · Reusable Playbooks · Training and Knowledge Transfer

Consumer Credit Risk · Merchant Sales-Based Financing · POS and Settlement Analytics · Acquisition Attribution and Merchant CAC · Unit Economics · Pricing and Offer Strategy · Portfolio Analytics · Servicing Analytics · Auto Finance · Credit-Bureau and Metro 2 Furnishing · FCRA · CECL · PD/LGD/Expected Loss · Customer Retention and Lifecycle Analytics · CPI · SCRA · Loss Mitigation · Syndicated Credit · Federal Bank Supervision · Remediation and Customer-Impact Analytics

• Bachelor of Arts in Economics, Minor in Mathematics — Virginia Tech, 2005–2009
• SAS Base Programming Certification — 2009
• SAS Advanced Programming Professional Certification — 2009
• Lean Six Sigma Yellow Belt — Office of the Comptroller of the Currency, 2016
• Associate Citation in Project Management — George Washington University, 2013

A system is not complete when the code runs or the output is produced.

A system is complete when the relevant stakeholders can understand:

• what question was being answered;
• which data were used;
• how the logic operated;
• which assumptions and parameters mattered;
• how the output was tested;
• which controls were applied;
• what exceptions remain;
• what decision is required;
• how the result should be implemented;
• how ownership and follow-through will be maintained.

Data integrity before modeling
Lineage before inference
Validation before reliance
Strategy testing before implementation
Reason codes before unexplained outcomes
Reconciliation before closure
Executive interpretation before action
Documentation before handoff

The objective is not complexity for its own sake. It is analytical work that is transparent, reproducible, governable, and useful.

I also use generative AI as a controlled accelerator for synthesis, narrative development, presentation refinement, documentation, and risk-control ideation—while retaining source verification, human judgment, and accountability.

All public repositories use synthetic, anonymized, or demonstration data.

These projects do not expose:

• customer or personally identifiable information;
• employer-owned code;
• production credit policy;
• proprietary remediation rules;
• confidential model inputs;
• regulated operational pipelines;
• internal systems or restricted documentation.

The repositories are designed to demonstrate transferable methodology, enterprise architecture, validation discipline, governance, documentation quality, and executive communication in a public setting.

• LinkedIn
• GitHub
• Merchant Sales-Based Financing Strategy Simulator
• Enterprise Credit Decisioning Strategy Simulator
• Survival Strategy Framework

Analytics · Applied Data Science · Decision Systems · Consumer Credit

Logic, controls, validation, evidence, and interpretation travel together.