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Quantitative Developer

Cititec
London
2 weeks ago
Applications closed

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Quant Developer

Pricing & Risk Technology

Commodities Trading

London | Full-Time, Permanent

Overview

Our client is a leading energy trading firm who are looking for a Quant Developer to join the Trading Technology group, focusing on the design, enhancement, and maintenance of a Python-based options pricing and valuation library. This system supports risk and valuation workflows across Oil, Power, Gas, and Equities, delivering real-time and end-of-day analytics for traders, risk, and quant teams.

The role suits a technically strong quant engineer with expertise in Python, derivatives pricing, and financial mathematics - ideally with exposure to energy and commodity markets.

Key Responsibilities

  • Develop, maintain, and extend Python pricing and risk libraries for options and structured derivatives (APOs, CSOs, ULDs, P1X).
  • Implement and calibrate pricing models (vanilla and structured) and ensure alignment with front-office risk systems.
  • Design and maintain volatility surface calibration workflows, manage market data (curves, vols, correlations), and build robust fallback and proxy mechanisms.
  • Enhance library performance, calibration routines, and diagnostics; contribute to regression testing and CI/CD pipelines.
  • Act as SME for pricing models within the risk and trading tech stack, supporting global teams and resolving production issues.

Key Skills & Experience

  • 5–10 years' experience in quantitative development within Investment Banking, Finance, or Commodities (energy, oil, gas, power, etc).
  • Advanced degree (MSc/PhD) in Maths, Physics, Financial Engineering, or related field.
  • Deep understanding of option pricing theory (Black-Scholes, local/stochastic volatility, Monte Carlo).
  • Expert Python developer with strong numerical and vectorized coding skills (NumPy, SciPy, Pandas).
  • Experience building and calibrating volatility surfaces and handling risk measures (Greeks, VaR, sensitivities).
  • Strong background in stochastic calculus, numerical methods, and optimization.

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