Pricing Quant

iO are currently seeking a highly skilled Senior Pricing Quant to join a client's innovative team within financial services. This opportunity is ideal for professionals passionate about developing cutting-edge algorithms that drive impactful trading solutions across global markets. The role offers a dynamic and collaborative work environment, recognised for its commitment to continuous innovation, sustainable growth, and a supportive corporate culture that values expertise and creativity. This strategic position focuses on designing and deploying AI-powered pricing models for emerging markets fixed income and rates products. It provides a unique chance to influence the future of electronic trading technology on a worldwide scale, working at the forefront of market microstructure and machine learning advancements. The successful candidate will be instrumental in shaping trading strategies for some of the most complex and evolving financial instruments, significantly contributing to technological leadership and success.

Key Responsibilities:

Develop and lead the implementation of advanced AI/ML pricing algorithms Create and refine models capable of functioning effectively in low-liquidity, high-volatility FX environments with sparse data conditions. Build real-time, pre-trade pricing engines, integrating alternative data sources Collaborate with traders and regional teams to validate model outcomes and incorporate market insights. Conduct rigorous testing, performance analysis, and calibration to ensure model robustness. Contribute to the expansion of the AI pricing platform, ensuring models adhere to global architecture standards. Work closely with technology teams to facilitate low-latency deployment in live trading environments.

Essential Skills & Experience:

At least five years' proven experience in quant research, algorithmic pricing, or financial machine learning within trading, fintech, or related finance sectors. Deep knowledge of fixed income markets, yield curve strategies, and credit or rates instruments. Proficiency in Python and relevant ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, or TensorFlow). Extensive experience working with noisy, sparse datasets in financial markets. Track record of deploying and maintaining models within live, high-speed trading or pricing systems. Understanding of government or credit bond market structures with a regional emerging markets focus is advantageous.