Oxford PhD (DPhil in Mathematical Physics). I build structured, reproducible modelling and data workflows in Python — spanning ML pipelines, strategy research/backtesting, and interactive simulation demos.
I’m interested in roles across quantitative modelling, data science / analytics, and risk / research engineering.
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Imbalanced Credit Default ML Pipeline (LogReg / RF / XGBoost)
End-to-end pipeline: data download → cross-validated model selection (PR AUC) → threshold tuning → plots + metrics.
Example results: ROC AUC ~0.77, PR AUC ~0.55; recall improved from ~35% → ~59% via imbalance-aware evaluation + threshold optimisation.
Repo:ml_pipeline -
Monday Range — Research + Backtest Framework
Research module quantifies conditional outcomes for a weekly liquidity pattern; backtest module includes risk-based sizing and layered exits.
Repo:monday_range -
Drone Radar Interception Simulation (HTML Canvas)
Lightweight browser demo: real-time simulation loop, radar sweep detection, 1:1 target assignment, interception dynamics; deployable via GitHub Pages.
Repo:drones
Python (NumPy, pandas, scikit-learn, XGBoost), reproducible experiment structure, basic time-series tooling, plotting (matplotlib).
Also: JavaScript/HTML (interactive demo), some MATLAB/C++ (older projects).
- GitHub: https://github.com/dnpjr
- LinkedIn: https://www.linkedin.com/in/daniel-pajer/
- Email: dancer-bout.6v@icloud.com
This profile is a living portfolio: projects and write-ups are being expanded with robustness checks (costs/slippage, walk-forward validation, sensitivity analysis) and additional modelling examples.