Quantitative Analyst -Cash Equity Algorithmic Trading - Director

We are seeking a strategic Senior Quantitative Analyst to develop and scale our cash equity algorithmic trading electronic execution platform. In this role, you will leverage cutting-edge AI technologies and mathematical methods to research EMEA equity market microstructure and drive platform innovation. If you thrive on complex quantitative deliverables and want to shape our directional trading strategy, this role offers significant growth and impact.

Responsibilities

• Build and scale our cash equity electronic execution platform using Java, Rust, and advanced AI technologies to power the next generation of algorithmic trading systems.
• Conduct EMEA equity market microstructure research, applying advanced statistics, probability, and mathematical finance to extract actionable trading insights.
• Backtest and prototype quantitative research strategies, leveraging Python and kdb+ to validate and optimize algorithmic trading models.
• Monitor and analyze client trading performance, delivering quantitative insights that optimize execution quality and client outcomes.
• Partner with Sales Trading, Execution Advisory Services, and technology teams to align our platform capabilities with evolving business needs.
• Collaborate with control functions—including Risk, Compliance, Legal, and Finance—to ensure robust governance and maintain a strong control infrastructure.
• Maintain a focus on risk management and ethical practices, ensuring trading decisions protect our clients, reputation, and assets.
• Complete and maintain all required professional registrations and licenses within the designated timeframes to support regulatory compliance.
  
  

Required qualifications and skills:

• Extensive background in quantitative modeling or analytics, ideally within the financial sector, focusing on algorithmic trading systems.
• Deep proficiency in Java and Python, with a strong foundation in software design and development principles.

Proven experience applying probability theory, statistics, and mathematical finance to solve complex analytical equations and analyze market data.

Demonstrated experience utilizing AI technologies to design and build high-performance trading applications.

Master's or PhD degree in a quantitative field (such as Mathematics, Financial Engineering, Physics, or Computer Science), or equivalent practical experience in a relevant field.

Strong written and verbal communication skills, with the ability to explain complex quantitative concepts to technical and non-technical partners.

Beneficial skills and qualifications:

• Hands-on experience with Rust and kdb+/Q for real-time data analysis and high-frequency trading.
• Familiarity with EMEA equity market microstructure and cash equity execution dynamics.
• Direct experience collaborating with risk management, compliance, or legal teams in a regulated global environment.
  
  

What we offer

• You will grow your career locally or explore international opportunities, supported by our investment in your professional development through training and mentorship programs.
• You can expect a flexible work environment with our hybrid model, which blends in-office collaboration with remote flexibility to support your work-life balance.
• You'll thrive in an innovative, inclusive culture where diverse perspectives are celebrated, and you will collaborate closely with world-class sales trading, execution advisory, and technology professionals.
• You can leverage comprehensive wellness benefits, including medical coverage, mental health resources, parental and family support, and competitive retirement planning.
  
  

If you are ready to drive innovation in algorithmic trading and make a global impact, apply today to join our team at Citi.

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Job Family Group:

Institutional Trading

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Job Family:

Quantitative Analysis

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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