Head of Quantitative Modelling & Research

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Job Description:

Roles & Responsibilities

Options Market Making, Calibration & Smile Modeling

Develop and own the quantitative infrastructure for quoting and risk managing vanilla and exotic options, including:

Real-time volatility surfaces

Greeks engines

Market-making and execution algorithms

Lead implementation of arbitrage-free volatility smile and skew models , including:

Smile parameterisation techniques : e.g. SVI, SABR, and Fengler’s arbitrage-free smoothing approaches

Local volatility models : Dupire local volatility for smile-consistent pricing and delta-hedging

Mixed local/stochastic volatility models : for capturing dynamic skew behaviour under stressed conditions

Build robust model calibration pipelines to liquid market instruments (e.g. vanilla options, forwards, futures) ensuring:

Fast convergence

Numerical stability

No calendar, butterfly, or vertical spread arbitrage

Extend volatility modelling to handle long-dated exotic derivatives :

American barriers, Asian accumulators, spread options, TARFs

Currency-denominated option structures with quanto and correlation features

Term Structure & Correlation Modelling

Develop multi-factor forward curve models for commodities and currencies:

Gabillon Two-Factor Model for capturing commodity forward curve dynamics

Schwartz-Smith or CIR++ extensions for interest rate and inflation-linked exposure

Model and estimate cross-asset correlations , particularly between:

Commodities (oil, palm, soy, energy, etc.)

Currencies (USD, CNY, MYR, INR, etc.)

Freight and storage costs

Integrate correlation modeling into:

Structured products

Portfolio VaR / CVaR frameworks

Basis risk hedging strategies

Real Assets & Physical Optionality

Build stochastic optimization and valuation frameworks for:

Crushing/refining spreads (e.g. soybean crush, palm kernel crush)

Storage and logistics assets as American swing options

Real-time asset monetization tools using Monte Carlo simulation, real options valuation, and basis path modeling

Incorporate physical constraints (capacity, delivery time, transport) into derivatives-driven optimization

Ideal Candidate

PhD or Master’s in a quantitative field (Mathematics, Financial Engineering, Physics, Computer Science)

Background in commodities markets (energy, agri , metals)

Experience building physical-real optionality models

Exposure to algorithmic quoting engines and real-time market data feeds

Understanding of machine learning techniques for market regime switching or signal generation

10+ years of experience in:

Quantitative research for derivatives trading or market making

Building volatility surfaces , smile models , and calibration tools

Exotic option pricing in commodity, currency, or hybrid markets

To apply, please submit your resume and cover letter outlining your interest for this role.