Statistical Modeling Subnational Economic Resilience
Classified 117 Philippine provinces and cities into two resilience typologies using seven years of official economic accounts — validating the structure with MANOVA before clustering.
Data Manager · Data Quality & Reporting Systems
Cleaner data. Clearer reporting. Workflows teams can trust.
I clean, validate, and structure business data for dashboards, audits, and repeatable reporting workflows.
4,400+ Records cleaned and documented
₱2M+ Budget reviewed for audit and reconciliation
$5M+ Sales dataset analyzed
QA Focused workflows
Currently at GSH Investments
Mark Anthony Nene
Data Manager focused on data quality, reporting workflows, analytics support, and audit-ready documentation.
Primary focus Data cleaning, validation, reporting workflows, and operational QA.
Working style Structured, careful, documented, and easy to review.
Tools & Focus Areas
Practical tools for data cleaning, reporting, QA, and analytics support.
Grouped for quick review without turning the tools section into a long paragraph.
Power BI Excel Google Sheets SQL Python Data Cleaning Reporting Audit Workflow Power BI Excel Google Sheets SQL Python Data Cleaning Reporting Audit Workflow Power BI Excel Google Sheets SQL Python Data Cleaning Reporting Audit Workflow
Dashboards KPI Design JSON Parsing ETL Documentation Automation QA Business Analysis Dashboards KPI Design JSON Parsing ETL Documentation Automation QA Business Analysis Dashboards KPI Design JSON Parsing ETL Documentation Automation QA Business Analysis
Window Functions Forecasting Data Validation Process Design Operational Accuracy Skip Tracing Structured Systems Decision Support Window Functions Forecasting Data Validation Process Design Operational Accuracy Skip Tracing Structured Systems Decision Support Window Functions Forecasting Data Validation Process Design Operational Accuracy Skip Tracing Structured Systems Decision Support
Selected Projects
Case studies in data cleaning, reporting, SQL, and analytics.
Each case study shows the problem, approach, tools used, and final output.
Statistical Modeling 
RevOps Analytics Real Estate Lead Analytics
An end-to-end Python pipeline that cleans a messy real-estate lead export, measures marketing ROI by source, and ranks leads into a data-driven call-first list — built on synthetic data to protect private records.
Data Engineering TMDb Data Engineering Workflow
Structured a movie analytics dataset by cleaning nested metadata, organizing entities, and documenting the workflow for reporting use.
Explore the Portfolio
Quick links to the main sections of my work.
Review my experience, results, resume, and contact details without going through one long page.
Current work
Current work in real estate data operations, QA, and reporting support.
Data Manager · Real Estate Data Specialist
Open experience Manage daily public-record data workflows for real estate prospecting, including extraction, cleaning, skip tracing, verification, and QA.
Results
Results and credibility markers.
Applied Mathematics Background
Quantitative training that supports careful data review, statistical thinking, and structured analysis.
Real Estate Data Operations
Hands-on experience in public-record extraction, data cleaning, skip tracing, QA, and workflow improvement.
Analytics & Reporting Tools
Practical use of Power BI, SQL, Python, Excel, and Google Sheets for reporting and analysis workflows.
Recent outcomes
Four results across data cleaning, analytics, reporting, and audit work.
- Identified a 230,000x output gap between urban and island economies and documented the pattern for clearer economic analysis.
- Built a dashboard for a $5M+ sales dataset to review discount behavior, category performance, and revenue trends.
- Created a Python cleaning workflow for 4,400+ records with documented checks and audit-friendly outputs.
- Supported audit and reconciliation work for a ₱2M+ budget with organized documentation and compliance checks.
Common problems
Common problems I help solve.
Unclean source data
Records that need deduplication, validation, standardization, and review.
Limited reporting visibility
Data that needs clearer KPIs, dashboards, or business framing.
Weak documentation
Processes that need repeatable steps, clean handoff, and reviewable outputs.