Quantitative Finance · Risk Analytics · Financial Data Science
$ whoami → engineer turned quant — MSc Financial Mathematics @ KTH, May 2027
Final-year MSc in Applied and Computational Mathematics (specialisation in Financial Mathematics) at KTH, Stockholm, graduating May 2027. I build and backtest quantitative models in Python and R for market risk, credit risk, and energy markets, and I publish the code on GitHub.
I came to finance after seven years in engineering and cost estimation. That work wasn't directly in finance, but the skills transfer: Monte Carlo simulation, regression modelling, forecasting, and making decisions from data. Those are the skills I still reach for in my quantitative work.
Graduate roles in:
- Risk Analytics — market risk (VaR / Expected Shortfall), credit risk (IRB PD/LGD/EAD)
- Portfolio Management
- Financial Data Science
- Quantitative Analysis
Open to graduate positions, internships, and master thesis projects from 2027.
Programming — Python (pandas, NumPy, scikit-learn, statsmodels, Streamlit) · R · SQL · MATLAB · VBA
Quantitative methods — time series (ARIMA/SARIMA, GARCH/EGARCH, Prophet, LightGBM) · GLM · high-dimensional regression (Ridge, Lasso, PCR, PLS) · DCC-GARCH and copulas · Monte Carlo and discrete-event simulation
Financial & risk modelling — Basel III IRB (PD/LGD/EAD) · VaR and Expected Shortfall · Kupiec and Christoffersen backtesting · credit scorecards · insurance pricing · stochastic calculus
Tools — Tableau · Advanced Excel · Git/GitHub
| Project | Description | Stack |
|---|---|---|
| nordic-electricity-forecasting | Day-ahead Nord Pool price forecasting with a ten-model comparison, ranked on an accuracy-versus-compute Pareto frontier. Compares gradient-boosted trees, foundation, deep, and classical models with leakage-free features, expanding-window backtests, CRPS and pinball scoring, and Diebold–Mariano tests. | Python |
| cross-commodity-energy-trading | Spread economics, DCC-GARCH correlation, and t-copula VaR across Brent, TTF gas, EUA carbon, and European power, to measure portfolio tail risk and cross-commodity spreads. | Python |
| Austrian-Daily-Electricity-Load-Forecast | ARMA modelling and a 31-day out-of-sample forecast of Austrian electricity load, with model diagnostics and forecast evaluation. | Python |
| freq-anomaly-detection | Rolling z-score and CUSUM anomaly detection on Nordic grid frequency, validated against ENTSO-E outage records. | Python |
| Project | Description | Stack |
|---|---|---|
| nordic-power-market-risk | Decision and risk system for a battery in the Swedish SE3 zone: MILP dispatch of energy and reserve capacity (FCR/aFRR/mFRR) across day-ahead, imbalance, and reserve markets, gated on CVaR and drawdown tail-risk limits and settled against observed prices. Probabilistic quantile forecasting (LEAR) drives the optimizer, with a walk-forward P&L netting EUR 483,956 versus EUR 86,516 for a heuristic benchmark. | Python, Docker |
| credit-risk-model-validation-workbench | Regulatory credit-risk & model-validation workbench: IFRS 9 expected-credit-loss pipeline and independent validation over a frozen Freddie Mac cohort — PD/LGD/EAD, staging, six-effect reconciliation, governance, monitoring, and a causal/fairness analysis. 35 modules, 190 tests, mypy --strict clean. |
Python |
| var-es-risk-engine | FRTB-aligned VaR and Expected Shortfall engine: GARCH volatility, Kupiec and Christoffersen backtesting, and a Streamlit dashboard for risk reporting. | Python |
| credit-risk-pipeline | Basel III IRB credit scoring pipeline: CatBoost, XGBoost, and LightGBM PD models with 0.58 out-of-time Gini, SHAP explanations for adverse action, served through FastAPI and Streamlit. | Python |
| lgd-ead-irb-modelling | IRB LGD and EAD capital models for Fannie Mae mortgages, aligned with CRR/EBA requirements, with a live Streamlit validation dashboard. | Python |
| Project | Description | Stack |
|---|---|---|
| project-1-high-dimensional-regression | PCR, PLS, Ridge, and Lasso with multi-split inference on a 4,088-predictor genomics dataset, comparing shrinkage methods on prediction error. | R |
| google-stock-volatility-forecasting | ARMA mean dynamics plus GARCH volatility clustering on Google stock returns, with volatility forecasts and residual diagnostics. | Python |
| project-2-glm-insurance-pricing | Multiplicative Poisson and Gamma GLMs for pure-premium insurance pricing, with model selection and rate relativities. | Python |
| spare-parts-optimization | Minimises expected backorders under a budget constraint using marginal allocation and dynamic programming. | MATLAB |
| Instacart | Customer segmentation and market-basket analysis on Instacart orders, with clustering and association rules. | Python |
| Customer-Analytics-Preparing-Data-for-Modelling | Cleaning, feature engineering, and validation of messy customer data for modelling. | Python |
| Project | Description | Stack |
|---|---|---|
| fixed-income-curve-engine | Yield-curve construction and fixed-income pricing built from scratch in Python, cross-checked against QuantLib. Term-structure models (Hull-White, G2++), SABR volatility smiles, Svensson calibration, and interest-rate risk: DV01, duration and convexity, key-rate duration, and delta VaR/ES. | Python |
| Option_Pricing | Black-Scholes pricing and Greeks from first principles: analytical and finite-difference Greeks, implied-volatility inversion, a volatility surface, CRR binomial cross-check, American early-exercise premium, and delta-hedging P&L, calibrated to OMXS30. | Python |
| Continuous-Time-Markov-Chains | Continuous-time Markov chain model of ferry reliability under competing maintenance strategies, validated by two independent simulation approaches. | MATLAB |
Early data-science work: Exploring-Airbnb-Market-Trends · Analyzing-Crime-in-Los-Angeles · Investigating-Netflix-Movies · Visualizing-the-History-of-Nobel-Prize-Winners · Python-Data-Cleaning
Project Engineer (2021–2025) Coordinated a USD 5.59M ERP upgrade with zero downtime; applied Six Sigma DMAIC to raise radio-system reliability from 72.15% to 99.46%.
Estimator (2018–2021) Built a VBA Monte Carlo cost model that replaced commercial software; produced bid benchmarks used across the portfolio.
Management Trainee (2017–2018) Built regression-based battery-lifetime models adopted as the facility's standard replacement-planning tool.
- MSc Applied and Computational Mathematics (Financial Mathematics) — KTH Royal Institute of Technology, 2025–2027 · GPA 4.06/5.00
- MBA — Institut Teknologi Bandung, 2020–2021 · GPA 4.00/4.00, Cum Laude
- BSc Electrical Engineering — Universitas Indonesia, 2012–2016 · GPA 3.67/4.00, Cum Laude
CFA Level I candidate (Aug 2026) · Project Management Professional (PMP) · PRINCE2 Practitioner · Six Sigma Green Belt · Qualified Risk Management Officer
Indonesian (native) · English (full professional) · Swedish (beginner)
