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🧪Data Scientist

Quantitative Analyst

Citi · London United Kingdom
// classified as
Data Scientist (Modeling, experiments, research.)
posted
1d ago
location
London United Kingdom
languages
java, python
tools
> stack
javapython
> education
phd
> description

We are seeking a Quantitative Analyst to join our EMEA Electronic Execution team, driving microstructure research, algorithmic trading analysis, and platform development for Cash Equity. In this high-impact role at the intersection of research and technology, you will shape how our electronic execution strategies perform across EMEA equity markets. You will collaborate directly with trading, advisory, and technology teams to deliver research and build systems that influence real market outcomes.

Responsibilities

  • Research and analyze EMEA equity market microstructure using mathematical finance, statistics, and probability to generate actionable insights for the algorithmic trading business.
  • Design and backtest quantitative research projects focused on algorithmic trading strategies, using Python and kdb to prototype and validate models.
  • Build and support the electronic execution platform for our cash equity algorithmic trading business, developing production-quality components in Java.
  • Monitor and analyze client execution performance, identifying patterns and translating findings into improvements that directly benefit client outcomes.
  • Collaborate with Sales Trading and Execution Advisory Services to align quantitative research with commercial execution needs.
  • Partner with Legal, Compliance, Risk, Audit, and Finance teams to maintain robust governance and control standards across all deliverables.
  • Assess the risk and reward profile of business decisions, applying sound judgment to protect both clients and the firm's reputation.

Required qualifications and skills

  • Hands-on experience in execution algorithm development, quantitative modeling, or analytics, preferably within financial services.
  • Proficiency in Java, used to develop and maintain production systems within an electronic trading environment.
  • Proficiency in Python and kdb, applied to quantitative research, backtesting, and data analysis.
  • Master's or PhD degree in Financial Mathematics, Computer Science, Physics, or a closely related discipline, or equivalent practical experience in a relevant field.
  • Clear and concise written and verbal communication skills, with the ability to present complex quantitative findings to both technical and non-technical audiences.

Beneficial skills and qualifications

  • Familiarity with EMEA-wide equity markets and cross-asset electronic execution.
  • Experience working in a high-impact role collaborating directly with trading, advisory, and technology teams.
  • Understanding of robust governance and control standards within a global financial institution.

What we offer

  • You will work at the forefront of algorithmic trading, where your research and engineering directly shape market outcomes.
  • You can expect a hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility alongside meaningful team collaboration.
  • You'll thrive in a collaborative, performance-driven environment with direct exposure to EMEA-wide equity markets and cross-asset electronic execution.
  • You can leverage continuous learning and professional development opportunities to deepen your technical and quantitative skills.
  • You can access competitive financial wellbeing packages, global employee programs, and locally tailored benefits to support your work-life balance.

Apply now to bring your quantitative expertise to one of our most technically driven trading teams and help define the future of electronic execution across EMEA equity markets.

#LI-COF

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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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