The story behind a number
What changed, why it changed, and what management or an investor should do next.
The person behind the analysis
I like finance most when the numbers lead somewhere.
My path started in electronics and telecommunications engineering, moved through technology management, and became firmly centered on finance. That combination still shapes how I think: understand the system, find the important drivers, and make the result useful.
01 / EngineerLearn how systems work.
→ 02 / Finance studentLearn how capital moves.
→ 03 / AnalystTurn evidence into decisions.
Why finance
It combines structure, judgment, and a constantly changing story.
I was first drawn to finance because it gave business decisions a language: cash flow, risk, return, and value. Graduate study at Gies deepened that interest through asset management and data analytics, while work across pharmaceutical finance, nonprofit analysis, economic research, and teaching showed me how different the same numbers can look depending on the decision being made.
The part I enjoy most is moving from calculation to interpretation. A forecast is useful when it explains the drivers. A valuation is useful when the assumptions are visible. A dashboard is useful when someone knows what to investigate next.
That is also why I build. The CFO Decision Lab, Wealth Planning Simulator, project case studies, and Finance with Faiz are different ways of making financial reasoning easier to explore and discuss.
What keeps me curious
Questions I naturally keep pulling apart.
Not a list of buzzwords—these are the recurring questions behind the work I choose to build and write about.
Markets meeting businesses
How rates, growth, and industry shifts eventually appear in revenue, margins, cash flow, and value.
Useful financial technology
Where Excel, SAP, Power BI, Python, SQL, or AI can remove friction without replacing judgment.
Practical finance toolkit
What I can do, how I approach it, and what comes out.
Software matters, but only in context. This toolkit is organized around the financial work rather than a long row of technology badges.
Planning & performance
Budget-to-actual analysis, driver-based forecasts, spending review, working-capital thinking, reconciliations, and variance investigation.
- Typical output
- Management reporting and an actionable variance view
- Tools in context
- Excel · SAP S/4HANA · Power BI
Valuation & investment research
DCF reasoning, financial-statement analysis, scenario and sensitivity work, company filings, macroeconomic context, and thesis development.
- Typical output
- Financial model, investment memo, or recommendation
- Tools in context
- Excel · Bloomberg · Capital IQ · Python
Data-enabled finance
Structuring messy information, automating repeatable analysis, exploring time-series and cross-sectional data, and communicating patterns clearly.
- Typical output
- Reliable workflow, dashboard, or analytical dataset
- Tools in context
- Python · SQL · R · Power BI
A more personal note
I want the work to be rigorous—and still inviting enough to explore.
Finance can become unnecessarily intimidating when the assumptions are hidden or the language is unclear. I enjoy making the logic visible, whether that is for a manager, an investor, a student, or someone simply trying to understand their money better.