Available for Roles in SaaS & FinTech

Strategic Finance &
Revenue Operations

GTM & FP&A Analyst with 5+ years of experience in High-Growth SaaS. I bridge the gap between Corporate Finance and Sales Ops using SQL, Python, and Driver-Based Modeling to optimize CAC, LTV, and Profitability.

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Strategic & Technical Toolkit

GTM & Revenue Strategy
  • SaaS Metrics (ARR, NRR, Churn)
  • CAC Payback & LTV Analysis
  • Pricing Strategy & Deal Desk
  • Sales Commission Design
Corporate FP&A
  • Annual Budgeting & Forecasting
  • 3-Statement Modeling
  • OpEx/CapEx Planning
  • Financial Close & Consolidation
Data & Analytics Stack
  • SQL (Data Mining & Warehousing)
  • Python (Pandas, Predictive Analytics)
  • Visualization (Power BI, Tableau)
  • CRM/ERP (Salesforce, NetSuite)

From Reporting to Value Creation

I don't just report numbers; I drive business expansion. With experience at Tapcheck and Emcentrix, I have partnered with Sales and Executive leadership to deliver real-time pipeline insights that secure capital and accelerate growth.

Whether it's optimizing pricing to boost gross margin by 12% or automating commission structures to lower CAC, I use data to turn Finance into a strategic advantage.

Read my take on the Future of FP&A →

Selected Case Studies

Healthcare Payment Optimization Engine

SQL Stored Procedures Power BI DAX Cost Analysis
Payment Dashboard

The Business Problem

Rising transaction costs due to paper checks ($3.50/txn) and poor provider experience due to slow payment latency.

The Solution

Built a SQL Stored Procedure to automate "High Risk" provider detection and a Power BI dashboard that identified $15k in potential savings.

View Dashboard & Code →

SaaS Revenue Intelligence Engine

GTM Strategy SQL Cohort Analysis LTV/CAC
Retention Heatmap

The Business Problem

High acquisition costs were masking a "leaky bucket." Needed to diagnose churn to improve the LTV/CAC ratio for a high-growth SaaS product.

The Impact

Built a Python/SQL Cohort engine. Identified a "Month 4 Drop-off," leading to a strategy shift that improved projected LTV by 15%.

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Enterprise Forecasting Transformation

Corporate FP&A Power BI Automation Budgeting
Dashboard

The Business Problem

Forecasting across 16 business units ($500M+ portfolio) was manual and disconnected, leading to inefficient capital allocation.

The Impact

Automated data pipelines and dashboards improved forecast accuracy by 67% and reduced the monthly close effort by 35%.

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Credit Risk Modeling

Python Predictive Analytics Risk Mgmt
Confusion Matrix

The Question

How can we accurately predict borrower default risk to minimize bad debt exposure while maintaining loan velocity?

The Outcome

Used SMOTE and Random Forest to achieve 93% accuracy, identifying interest rates as the #1 risk factor for the portfolio.

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Resume
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