Each project here starts with a concrete business problem, a decision that needed structure, a question that standard reporting could not fully answer, or an analytical framework built to give leadership clearer choices. The library grows as new work is completed.
The problem: Finance teams need a structured, integrated budgeting framework that connects assumptions to P&L, tracks actuals monthly, and automatically generates variance analysis, without rebuilding the model from scratch every planning cycle.
The problem: Revenue grew but gross margin did not improve. Leadership needs to understand exactly where the margin went, and whether the issue is structural or controllable.
The problem: A business needs a forecast that reflects realistic uncertainty, and leadership needs to know which assumptions actually move the outcome, rather than treating every input as equally important.
The problem: Margin pressure is spreading across product lines. Leadership needs to understand where true pricing power exists, which segments are structurally profitable, and how different pricing and mix strategies affect overall results.
The problem: A business's P&L looks acceptable, but cash flow is unpredictable. Leadership needs to understand how changes in revenue timing, collection patterns, payment cycles, and margin affect liquidity, before they happen.
These projects demonstrate the advanced quantitative modeling background that informs my business work, applying mathematical programming and optimization to complex, real-world decision problems.
The problem: Every business faces decisions involving many variables and constraints simultaneously, pricing across product lines, resource allocation across departments, capacity planning across time periods. These are typically made with data and intuition. But in complex environments, a systematic optimization framework can find solutions that intuition alone would not identify.
A large-scale nonlinear optimization model of an agricultural sector, covering crop production decisions, water allocation across regions and water types, market prices, and trade. Built using Positive Mathematical Programming in GAMS. Used for policy scenario analysis including yield shocks, water restrictions, and import policy effects.
I am available for consulting projects, fractional FP&A engagements, financial modeling work, and strategic analysis. The more complex the question, the more structured analysis tends to help.
I ran a structured benchmark — Opus 4.8 vs Sonnet 4.6, tested across effort levels on a CFO monthly close task. The findings have direct implications for how FP&A professionals should use these tools in practice.
Read the Benchmark →