Counterparty Credit Risk Quant Technology Lead (Python/C++)

The Opportunity

Citi is seeking a senior Quant Technology Lead to drive the development of next-generation pricing and risk analytics platforms for the Commodities business within ACE team. This role sits at the intersection of Trading, Quantitative Research, Risk Management, and Technology, delivering the infrastructure and analytics that power pricing, valuation, and risk management across global commodities markets.

You will work directly with Front Office quants and trading teams to implement sophisticated quantitative models into scalable, high-performance production systems. This is a unique opportunity to combine quantitative understanding, software engineering excellence, and technical leadership while building strategic platforms used across the firm.

Your Role and Impact

As a Commodities Quant Technology Lead, you will lead the engineering efforts behind pricing and risk analytics for commodity derivatives. You will be responsible for translating quantitative models into robust production solutions capable of handling complex valuation and risk calculations at scale.

Beyond hands-on development, you will provide technical leadership across strategic initiatives, mentor development teams, and partner with senior stakeholders in Trading, Risk, and Quantitative Research. Your work will directly influence the firm's ability to manage risk, support trading activities, and deliver innovative products to the market.

Key Responsibilities

• Lead the design, development, and delivery of strategic quantitative analytics platforms supporting Commodities pricing and risk management.
• Develop and enhance high-performance analytics libraries using Python and C++ for valuation, sensitivities, scenario analysis, and risk calculations.
• Partner closely with Front Office Quants and Trading teams to integrate new pricing models and quantitative methodologies into production systems.
• Drive performance optimization efforts for large-scale analytics and computationally intensive pricing workflows.
• Architect scalable distributed solutions supporting intraday and end-of-day risk calculations.
• Establish and promote engineering best practices around software design, testing, automation, and CI/CD.
• Lead impact assessments, production releases, performance tuning, and platform modernization initiatives.
• Collaborate with Model Risk, Market Risk, and Regulatory teams to support governance and control requirements.
• Mentor and coach junior developers while serving as a technical SME for senior stakeholders.
• Drive technical discussions and influence architecture decisions across commodities analytics platforms.

Required Qualifications

• 14+ years of extensive experience developing quantitative analytics platforms within Capital Markets. Expert-level Python development with experience building production-grade analytics and risk systems.
• Strong C++ development skills, particularly in performance-sensitive environments.
• Proven track record delivering quantitative technology solutions for derivative pricing, risk, or valuation analytics.
• Deep understanding of software engineering principles, distributed computing, and scalable system design.
• Experience with Linux/Unix environments and development toolchains.
• Strong understanding of SDLC, DevOps practices, CI/CD pipelines, Git, and automated testing frameworks.
• Outstanding analytical and problem-solving skills with the ability to simplify complex quantitative concepts.
• Strong communication skills and experience working directly with senior business and quantitative stakeholders.
• Demonstrated experience leading technical initiatives and mentoring engineering teams.

Preferred Qualifications

• Experience with Commodities markets, including Energy, Oil, Gas, Power, Metals, Agricultural, or Environmental products.
• Understanding of commodity derivative pricing models, curve construction, volatility modelling, and risk methodologies.
• Knowledge of computational finance, numerical methods, Monte Carlo simulations, and optimization techniques.
• Experience supporting regulatory initiatives such as FRTB, Basel, Stress Testing, or Model Risk Management.
• Familiarity with distributed compute grids, cloud technologies, and large-scale analytical processing.