I Build Practical FP&A Tools, Models, and Decision Frameworks
I am a Toronto-based FP&A professional and financial economist. I turn complex financial questions into clear models and toolkits that help finance leaders and business owners make better decisions, work that sits at the intersection of practical business finance, quantitative rigour, and modern AI-assisted workflows.
Who I Am
I am an FP&A professional and financial economist based in Toronto, Canada. I build financial models, toolkits, and decision frameworks for businesses facing complex financial, strategic, or operational decisions, where the question is not just what the numbers say, but what they mean and what to do about them.
My path into finance runs through two distinct sources: graduate-level training in financial economics, econometrics, and mathematical modeling on one side, and hands-on corporate FP&A experience in a real business environment on the other. That dual foundation shapes how I approach financial problems, with both analytical rigor and practical business judgment.
Strategic FP&A & Finance Experience
My FP&A experience spans budgeting, forecasting, variance analysis, management reporting, margin analysis, and cash-flow planning, in a business environment with genuinely complex drivers: pricing dynamics, product and customer mix, gross margin pressure, capacity constraints, backlog, and working capital.
That experience taught me how financial planning works under real conditions: imperfect data, competing priorities, and leadership that needs clarity under time pressure. I have built financial models and planning processes under those conditions, designed to be understandable, defensible, and directly useful for decision-making.
Statistical, Econometric & Quantitative Foundation
My graduate training in economics and econometrics gives me analytical tools that rarely appear in standard FP&A work: estimating relationships between business variables, separating the effects of price, volume, and mix, evaluating whether a change in the numbers is meaningful or just noise, and quantifying uncertainty in a way that is useful for decisions, not just acknowledged.
This includes revenue-driver analysis, demand and sales forecasting, price sensitivity and elasticity thinking, regression-based modeling, and scenario analysis that reflects genuine uncertainty rather than arbitrary point estimates.
Optimization & Decision Modeling
Alongside my statistical background, I have deep experience in mathematical programming and optimization, finding the best allocation of resources, pricing strategy, or operational decision given a set of real-world objectives and constraints. This background shapes how I approach business decisions: not as isolated questions to be answered separately, but as integrated problems with tradeoffs that need to be modeled explicitly.
AI-Assisted Analytical Workflows
I use AI tools, including Claude, as part of a modern analytical workflow, structuring complex problems, drafting management narratives, improving model documentation, and building reusable frameworks more efficiently. AI does not replace financial judgment, business understanding, or modeling expertise. It amplifies them when applied carefully.
What Drives My Work
My goal is to help businesses make better decisions through creative, rigorous analysis, building the kind of decision-support work that turns complex financial questions into clear, actionable direction. The free tools on this site are the first layer of a growing toolkit library; advanced toolkits and custom builds extend the same thinking further.
That work is underpinned by a broader research background, including graduate-level economic research and large-scale optimization modeling. I mention it not as a separate direction, but because it explains the analytical depth behind the practical tools, the engine, not the product.