About Me

Finance, data automation, and retirement decision tools.

My Background

I’m Kevin Cho — a finance professional, CPA, and hands-on data builder with over 20 years of experience in real estate finance, accounting, forecasting, and executive reporting. My work combines financial judgment with practical technical execution across Python, SQL, Excel, Flask, PostgreSQL, machine learning, and cloud-hosted data applications.

What I Have Built

My current capstone project is Data by Kevin’s Retirement and Portfolio Planning Platform — a cloud-hosted financial decision engine designed to help users stress-test retirement timing, spending, portfolio risk, Monte Carlo outcomes, tax-aware withdrawal sequencing, cash-reserve tradeoffs, and market deployment conditions.

The platform turns user assumptions into a personalized retirement decision report, combining projection tables, downside-risk analysis, portfolio volatility insights, sensitivity testing, CDC market-deployment context, and practical next-step interpretation. My goal is to make complex retirement and portfolio questions easier to understand, test, and explain — without hiding the assumptions behind a black box.

Capstone Project

  • Retirement & Portfolio Decision Platform: Built a Flask/PostgreSQL-based planning engine that connects retirement projections, Monte Carlo survivability, portfolio return/volatility assumptions, tax-aware withdrawal logic, cash-reserve analysis, scenario saving, Stripe checkout, and personalized PDF-style report output into one integrated workflow.
    ➤ How the Report Works | Sample Report | Free Preview
  • Cash Deployment Cockpit with ML Overlay: Added a rule-based CDC market-deployment layer with machine-learning validation to compare market mood, deployment risk, entry opportunity, signal alignment, drawdown, moving-average distance, and credit/risk-on signals. The ML layer ranks historical and live candidate entry windows, helping challenge or confirm the CDC framework instead of relying on simple market timing rules.

Earlier Data Automation Projects

  • Automated CAM Reconciliation Platform: Leveraged SQL and Python to mirror Yardi’s recovery logic, reducing hours of manual work and eliminating inconsistencies.
  • Leasing Scenario Modeling Tool: Built a 5-year leasing projection pipeline integrating SQL, Python, and xlwings, allowing dynamic budget and forecast updates and vacancy tracking across 400 tenants.
    ➤ Executive Summary | Technical Walkthrough
  • CapEx Forecast Engine: Designed a unified input/output Excel template, powered by Python scripts, to consolidate actuals, budgets, and user-input reforecasts into a single dynamic summary report.

Career Objective

My mission is to build practical data platforms that improve decision quality. I’m especially interested in financial planning, portfolio risk, retirement readiness, and executive-level reporting systems where the value comes from connecting data, assumptions, workflows, model validation, and interpretation into one clear decision process.

Technical Skills & Tools I Leverage

  • Languages: Python, SQL, HTML/CSS, JavaScript, VBA
  • Frameworks & Libraries: Flask, Bootstrap, Pandas, NumPy, xlwings, Chart.js
  • Databases: PostgreSQL, SQLite, AWS Athena, DBeaver
  • Platforms: AWS EC2, GitHub, PyCharm, Nginx, Gunicorn, Excel
  • Applied ML & Analytics: XGBoost, feature engineering, walk-forward validation, candidate ranking, market-regime scoring
  • Applied Areas: Retirement modeling, Monte Carlo simulation, portfolio risk, tax-aware planning, cash deployment analysis, ETL automation, financial forecasting

How I Create Value

Over the last two decades, I’ve learned that strong financial decisions require more than reports — they require systems. Whether the problem is retirement planning, portfolio risk, cash deployment, leasing forecasts, CAM reconciliations, or capital expenditure tracking, the real value comes from turning scattered assumptions and manual workflows into structured, testable, and repeatable decision tools.

Final Thoughts

Data by Kevin is where I bring together my finance background and technical build experience. The goal is simple: turn complex financial questions into clearer decisions through transparent assumptions, practical modeling, ML-supported validation, and tools that people can actually use.