Teddy Zhao's Profile

Quantitative Researcher/Developer

Derivatives Pricing & Risk Analytics | C++/Python Expert | PhD Machine Learning

Quantitative Researcher / Developer specialising in production-grade pricing engines, risk management systems, and model validation frameworks for multi-asset derivatives. Expert in C++ and Python for building Monte Carlo simulators, volatility surface calibrators, and regulatory capital calculators. Strong foundation in stochastic calculus, statistical modelling, and financial engineering with direct applications to interest rate products, credit risk, and counterparty exposure management.

8+ years building high-performance quantitative systems for large-scale time-series and high-dimensional data. Experienced in translating complex models into robust, well-tested, governance-ready code that supports risk reporting, model validation, and regulatory compliance (Basel III, FRTB, SA-CCR). Proven ability to explain technical concepts to non-technical stakeholders and work across quantitative research, technology, and business teams.

Core Technical Skills

Financial Engineering & Derivatives

Pricing models: Black-Scholes, Local Vol, Stochastic Vol, Monte Carlo methods
Risk metrics: Greeks (delta, gamma, vega), VaR, Expected Shortfall, PnL attribution
Credit risk: CVA, SA-CCR, PFE simulation, counterparty credit exposure
Regulatory capital: Basel III, FRTB, model risk management (SR 11-7)
Products: IR derivatives, equity derivatives, FX options, credit derivatives

Programming & Systems

C++ (17/20): Expert-level for pricing engines, numerical libraries, low-latency systems
Python: Production-grade (NumPy, Pandas, SciPy, QuantLib, gs-quant)
Java, R, MATLAB, SQL
Software engineering: Git, CI/CD, unit testing, code review, SDLC best practices
High-performance computing: Vectorisation, parallelization, distributed systems

Quantitative Modelling

Stochastic processes: SDEs, Brownian motion, jump-diffusion, regime-switching
Statistical methods: Time-series analysis, Bayesian inference, ML for finance
Risk analytics: Stress testing, scenario analysis, model validation, uncertainty quantification
Optimisation: Convex optimisation, portfolio construction, hedging algorithms

Industry-Relevant Projects

Derivatives Pricing Engine (C++20)
Multi-asset Monte Carlo framework with automatic differentiation for Greeks calculation.
SA-CCR Capital Calculator (Python/C++)
Counterparty credit risk exposure profiles for IR, FX, and Equity derivatives.
Risk Model Validation Framework (C++17)
Automated testing and regulatory reporting for model governance and audit compliance.
Strategy Backtesting System (Java)
Multi-threaded backtesting engine for systematic strategies with transaction costs.
Fixed Income Analytics (Python)
Yield curve construction, duration/convexity, bond pricing, and ALM simulation tools.

Full portfolio

Academic Research (transferable to finance)

PhD Machine Learning, University of Birmingham
Focus: Statistical inference, stochastic optimisation, high-dimensional modelling

Publications with direct finance applications:

Regime detection algorithms → Market state identification for trading strategies
Latent variable inference → Factor models and correlation clustering for risk
High-frequency signal analysis → Microstructure noise filtering (HFT applications)
Uncertainty quantification → Model risk measurement and VaR backtesting

Google Scholar

Transferable research

Currently Seeking

Quantitative roles in:

Derivatives Pricing & Quantitative Development
Model Risk & Validation
Quantitative Analytics (Risk, Treasury, Capital)
Trading Systems & Risk Infrastructure

Additional information:

Location: London/UK (willing to relocate)
Visa: UK Global Talent (unrestricted right to work)
Availability: Immediate start

Contact

teddy.d.zhao at gmail dot com