Brisbane City, Queensland, Australia
I use Python to model market risk, backtest trading strategies, and optimize complex systems. My background spans energy market intelligence and digital asset trading. I build computational engines to quantify pricing volatility—whether simulating National Electricity Market (NEM) load-shedding or stress-testing digital economies. I also back this up with a solid background in front-line customer service. I don't just look at data screens; I know how to talk to people, translate complex technical ideas clearly, and resolve high-pressure problems directly. Core Competencies: Quantitative Analytics: Risk modeling, backtesting engines, and time-series forecasting. Systems Architecture: Digital twins, Monte Carlo simulations, and flow-state logic. Customer Operations: Technical troubleshooting, relationship management, and front-line support. Tech Stack: Python (Pandas, NumPy), SQL, and Mathematical Modeling.
Built a Python engine to model solar asset performance and energy market volatility. Used Pandas and NumPy to build a backtesting engine for National Electricity Market (NEM) trading strategies. Modeled pricing risk and load-shedding scenarios to optimize capital allocation. Designed data pipelines to ingest real-time energy pricing and simulate process adjustments based on grid demand. Wrote a script to normalize weather-variance data for solar yield forecasting. Reduced model latency and increased forecasting accuracy.
Travelled to China, traded on the side.
Executed systematic trading strategies across digital assets and equities. Managed capital allocation utilizing rigorous risk-parity frameworks and trade execution logic.
Designed digital economies using Monte Carlo simulations and agent-based modelling. Optimized supply-side incentive structures for system stability under high volatility. Used Machinations.io for flow-based logic modelling.
Built computational models using Python and Monte Carlo simulations to simulate resource flows, predict system failure points, and optimize throughput
Architected logic frameworks for digital twin environments and resource allocation mapping. Developed flow-state models to govern digital asset distribution using mass-balance and transient analysis logic. Analyzed high-frequency data to isolate structural bottlenecks and stress-test capacity limits under simulated volatility. Deployed feedback loops to maintain system stability mirroring industrial PID control mechanisms.