Senior Quant Analyst, Electronic Market Making & Rates

Citi; Paris; Sur place; EUR 150 000 - 205 000; Plein temps

Résumé du poste

Citi in Paris seeks an experienced Quantitative Analyst to join our Markets team, collaborating with traders, technologists, and structurers to advance electronic market-making and fixed income analytics.

You will design pricing, hedging and predictive models, apply machine learning and statistics, and deliver production-grade tools using Python, KDB/Q, Rust, and Java. Collaboration with risk, technology and business stakeholders is essential.

Qualifications

• Extensive experience in quantitative research, trading or analytics.
• Proven track record deploying models in electronic trading or market-making.
• Strong knowledge of machine learning, statistics and predictive analytics.
• Advanced programming skills in Python; familiarity with time-series databases and KDB/Q.
• Solid understanding of fixed income markets, especially Rates.

Responsabilités

• Research, develop and enhance electronic market-making models and hedging solutions.
• Build scalable analytics tools and production-ready models using Python, KDB/Q, Rust, and Java.
• Collaborate with Traders, Structurers and Technology to translate insights into trading outcomes.
• Identify opportunities to improve trading efficiency, execution quality and risk management.
• Contribute to governance, risk controls and data infrastructure across teams.

Connaissances

Machine learning

Statistics

Quantitative modelling

Predictive analytics

Python

KDB/Q

Time-series databases

Data engineering

Fixed income rates

Communication skills

Formation

Master's or PhD in Mathematics/Statistics/Physics/CS/Engineering

Outils

KDB/Q

Python

Rust

Java

Description du poste

Citi in Paris seeks an experienced Quantitative Analyst to join our Markets team, collaborating with traders, technologists, and structurers to advance electronic market-making and fixed income analytics.

You will design pricing, hedging and predictive models, apply machine learning and statistics, and deliver production-grade tools using Python, KDB/Q, Rust, and Java. Collaboration with risk, technology and business stakeholders is essential.