Curious by default, quantitative by choice
I am Milan Killian — a recent graduate in Commercial Economics on my way into quantitative finance, building things with markets, data and AI along the way.
The short
version
Economist by training, builder by temperament. During my Commercial Economics degree I discovered that the questions I cared about most were quantitative: why prices move, how you separate signal from noise, and how you prove — not claim — that something works.
So I started building: LLM classification pipelines, validated knowledge bases and agentic workflows for financial institutions. Everything on this site follows the same rule — deterministic where possible, human-in-the-loop where it matters, honestly evaluated.
My edge sits at the intersection: I speak the language of the business side, I write the code myself, and I know how to put LLMs and agents to work inside real team processes. Financial institutions are where those three come together.
The route
Graduated — Commercial Economics
Final thesis on deploying LLMs and AI agents within an organisation’s teams and processes to drive efficiency and optimisation.
Pre-master’s — Finance
Closing the gap to graduate-level finance: statistics, econometrics, asset pricing.
Duisenberg Honours Programme — Quantitative Finance
Master’s specialisation in quantitative finance: derivatives, risk, computational methods.
Quant & AI within financial institutions
Where research discipline meets production systems — and where my personal interests come together.
How I work
Question
Every strategy starts with a doubt. I challenge assumptions in markets, models, and my own code before I trust them.
Validate
Nothing ships without proof. Test suites, ground-truth evaluation and honest metrics separate evidence from anecdote.
Deploy
Ideas only count when they run. I build modular, production-ready systems, with risk controls wired in from day one.
Ownership
From concept to kill switch, the full chain is mine. I take responsibility for outcomes, not just outputs.
Compound
Every project feeds the next. Code, lessons, and discipline stack over time — that’s where the real returns are.
Still curious
Markets podcasts, poker maths, and an unreasonable number of side projects. The mosaic M on the home page? Click the bubbles.
Let’s talk
Open to quant-adjacent roles, internships and good conversations about markets, data and AI.