Hi, I'm

Leon

Current professional development · Wall Street Prep

Financial & valuation modelling · In progress

Bachelor of Commerce

Finance & Business Analytics · Mathematics Pathway
The University of Melbourne

How I work

I try to do careful, dependable work — whether I am selecting a driver for an M&A model or cleaning an analytics dataset. My aim is to make the assumptions as complete, accurate and transparent as I can.

I like to embrace AI as part of how I work because it can make research, analysis and iteration far more efficient. But I believe being able to do the work independently, understand the method and recognise the limitations of an AI-generated output matters even more.

Current & featured work

Long-form project stories covering what I built, how I approached the problem and why the work matters.

Current professional development · Wall Street Prep

Financial and valuation modelling

Building the technical foundation to move from financial statements to an explainable valuation range.

I'm currently developing practical modelling skills across three-statement forecasts, DCF valuation, trading and transaction comparables, M&A analysis and LBO mechanics—while focusing on model structure, assumptions and sensitivity analysis.

Three-statement modellingDCF and WACCComparable companiesM&A and LBO foundations

See what I'm learning ↗

Earlier transaction case · Nash Advisory × FMAA · 2026

EBOS Group acquisition case

Could Wesfarmers acquire EBOS at a defensible price while preserving balance-sheet capacity and creating value?

I completed this case before beginning Wall Street Prep, building an end-to-end acquisition view across strategic fit, historical performance, beta and WACC, DCF, trading and transaction comparables, synergies, financing, accretion and risk. After the course, I plan to revisit the model and polish its drivers, comparables, audit checks and sensitivity logic.

DCF and sensitivity analysisTrading and transaction comparablesSynergy and financing analysisPro-forma accretion and risk

See the original approach and refinement plan ↗

Team-led analytics · Airbnb listings · In progress

Airbnb Market Intelligence

Which listing characteristics genuinely affect price and popularity—and how do those relationships change by season and location?

As team leader, I'm structuring the data-cleaning and modelling process: audit every usable variable, test intuitive and less obvious drivers, investigate interaction effects, then build the model around genuinely comparable listings.

Data cleaningFeature screeningInteraction effectsComparable listings

Explore the project ↗

Start with every usable variable.

Let the evidence decide what matters.

PriceSeasonalityLocationMinimum stayListing characteristics

SME liquidity · Forecasting · Scenario modelling · 2026

SME Liquidity & Debt Capacity Analysis

When—and under what operating conditions—can an SME take on and repay debt without restricting normal operations?

I analysed historical account and cash-flow trends, built a sixteen-week Monte Carlo forecast with confidence intervals, and linked debt capacity to projected performance, liquidity protection and revenue-cost stress scenarios.

SQLPythonMonte CarloStress testing

View the detailed case study ↗

Revenue and cost stress-test heat maps from the loan repayment simulation

Performance → capacity → stress test → decision

Strategic advisory · Ashfords

Pricing and growth strategy

How can a service business use discounts to support growth without quietly giving away its margin?

I built a dynamic Excel model connecting pricing, service discounts, volume, break-even thresholds and profitability. Instead of presenting one recommendation, I made the trade-offs visible across scenarios and explained the decision logic to senior stakeholders.

Model the unit economicsTest discount scenariosIdentify break-even pointsPresent the commercial logic

Read the full project story ↗

Research strategy · NHIA

Funding opportunity strategy

How can a broad funding market become a repeatable pipeline rather than a one-off search?

I led a five-person team to review more than 50 government programs, define a consistent assessment structure and turn the research into a reusable database, prioritised shortlist and 20+ page advisory report.

Set decision criteriaStructure 50+ programsPrioritise opportunitiesCoordinate a five-person team

Read the full project story ↗

Experience

Commercial judgement starts with how a business actually runs.

March 2026 — present · Part-time

Ocycle · Melbourne

Administration Assistant

An operating role inside an e-bike business, where I see how customer decisions, accurate information and reliable processes shape commercial outcomes.

Customer insightCommercial operationsProcess disciplineSOPs

Read the customer

Listen for the real need behind an enquiry, identify the trade-offs that matter and translate product detail into a recommendation someone can act on.

Keep information decision-ready

Maintain accurate internal records and coordinate hand-offs across the team, learning how information quality affects service, speed and day-to-day decisions.

Improve repeatable work

Notice recurring friction, support clearer SOPs and make routine workflows easier to follow—small operational improvements that compound over time.

What I am taking forward

Good analysis starts before the spreadsheet: understand the commercial drivers, trace what is happening underneath the numbers and communicate a clear next step.

Education

Finance, analytics and a wider view.

The University of Melbourne · 2024—2027

Bachelor of Commerce

Finance & Business Analytics majors · Mathematics Pathway

Corporate valuationDerivative securitiesFinancial decision makingMachine learning & AI

Korea University · Academic exchange

Econometrics

Most valuable takeaways

Built and tested regression and forecasting models in R.
Selected variables and checked whether results made economic sense.
Diagnosed multicollinearity, heteroskedasticity and autocorrelation.
Learned to question outputs instead of accepting a model at face value.

Leon and a school friend at the 2023 Navy Blue Formal

Class of 2023

Before university · Pulteney Grammar School

The people behind the school years.

A short story about academic foundations, friendship and one very memorable formal night.

Visit my high-school story ↗

A small exchange lesson

Australians in Korea: don't automatically press this button.

In Australia, it asks the traffic light for permission to cross. In Seoul, this one activates an audio guide for visually impaired pedestrians — the lights often change automatically.

A roadside accessibility audio-guide button in Seoul — The button in question.

A chat explaining that Seoul traffic lights change automatically — One message later: mystery solved.

A funny reminder that familiar-looking systems do not always work the same way — useful in a new country, and in a new dataset.

Toolkit

Technical enough to build it. Human enough to explain it.

Finance

DCF valuation
Trading multiples
LBO modelling
Corporate valuation
Break-even analysis

Analytics

Python
SQL
R
Tableau
Machine learning
Monte Carlo simulation
Data visualisation

Working with people

Client communication
Stakeholder engagement
Team leadership
Executive presentations
Project delivery

Get in touch

Good work usually starts with a good conversation.

I'm open to internships and analyst opportunities across finance, advisory, economics and business analytics.

Email me ↗LinkedIn ↗