Top London Quantitative Analyst Jobs

Develop AI/ML-driven analytics, automation, data architectures, and sequential decision-making tools for Securities Services. Responsibilities span data processing, quantitative modeling, prototype-to-production development, balance-sheet and deposit optimization, visualization, and stakeholder presentations. The role collaborates with Quantitative Research, Technology, and data teams to solve complex commercial problems using machine learning, large language models, and cloud or HPC environments.

The Quantitative Research Analyst Intern will develop models, implement trading strategies, and conduct statistical analysis during an 11-week program.

Own and improve quantitative financial datasets supporting research and investment workflows. Design schemas, databases, pipelines, and DAGs; extend historical and high-frequency coverage; onboard and reconcile vendor data; and build automated validation, monitoring, dashboards, and investigation tools. Diagnose anomalies, resolve root causes, document standards, and collaborate with quantitative researchers, portfolio managers, vendors, and distributed global teams. The role requires substantial buy-side investment data experience, strong Python and SQL, and expertise in financial data quality.

Analyze digital asset market and trading data across venues, build latency-aware backtests and execution models, evaluate liquidity and strategy performance, and support live trading through PnL, inventory, exposure, and post-trade analysis. Develop Python tools and SQL data pipelines, investigate market microstructure and price discovery, and communicate research findings, assumptions, risks, and recommendations to traders, quantitative researchers, and engineers.

Develop and improve quantitative models for investment strategies, conduct statistical analysis, and implement trading models. Requires strong analytical skills and relevant degree.

Off-cycle quantitative research intern supporting systematic trading, financial engineering, statistical modeling, portfolio optimization, and derivatives pricing. Responsibilities include developing mathematical models and algorithms, valuing and hedging transactions, conducting alpha research, calibrating models, optimizing pricing, managing portfolio risk, and collaborating with traders, technologists, sales, and risk teams. Candidates gain hands-on global markets experience and may be considered for full-time employment after successful completion.

Supports quantitative risk and treasury research by analyzing data, backtesting strategies, maintaining software tools, evaluating model soundness, and developing, validating, and enhancing mathematical models and algorithms for financial applications.

Summer internship applying quantitative analysis, Python, machine learning, and frontier AI techniques to hedge fund research and trading problems. Interns may develop analytics libraries, trading strategies, signals, backtests, AI-enabled tools, LLM applications, and agentic systems while collaborating with quantitative, applied AI, and portfolio management teams. The program includes financial markets training, mentorship, and potential conversion to a graduate role.

Research and backtest quantitative trading strategies across global options markets. Analyze PnL attribution, market dynamics, volatility surfaces, and portfolio performance. Collaborate with traders to prototype and refine strategies, maintain reliable trading infrastructure, build scalable systems, and develop dashboards supporting discretionary trading decisions.

Design, build, deploy, and operate systematic interest rate swaps trading systems covering pricing, risk projection, hedging, and execution. Develop research infrastructure, backtesting and simulation tools, quantitative models, signal pipelines, and analytics over large datasets. Analyze trading data, improve strategies, support live trading, resolve incidents, and enhance monitoring and controls.

Conduct quantitative research supporting fixed-income portfolio construction, optimization, trading processes, and execution strategies. Collaborate with portfolio managers, traders, researchers, and technologists to develop systematic approaches, re-engineer portfolio processes, implement signals, and solve large-scale construction problems. Clearly communicate technical findings to senior stakeholders while delivering rigorous, practical investment solutions.

Validate and develop quantitative pricing, trading, valuation, and risk models across multiple asset classes. Responsibilities include reviewing C++ implementations, testing Python-based models, validating VaR and credit risk methodologies, developing historical datasets, conducting Monte Carlo and sensitivity analyses, calibrating model parameters, assessing complex transactions, and producing technical validation documentation using LaTeX.

Develop and maintain pricing and risk models and quantitative libraries for FX options. Design algorithms, APIs, risk frameworks, testing processes, and pricing engines using C++ and Python. Collaborate with traders, sales, risk teams, and international quant groups to explain model assumptions, support products, and improve quantitative tools. The role requires advanced knowledge of FX valuation, options pricing, probability, stochastic processes, and quantitative modeling.

Conduct quantitative research across equities, derivatives, and multi-asset strategies; develop proprietary datasets, signals, models, and production pipelines; perform robust backtesting and validation; publish actionable investment insights; and provide high-touch service to institutional clients.

Develop and support valuation models for convertible bonds and other asset classes within a product development team. Responsibilities include idea generation, product specifications, UI design, manual and automated QA, client-requested feature delivery, and collaboration with quantitative developers. The role provides training in financial markets, convertible bond structures, pricing models, and front-to-back capital markets processes, with potential progression into dedicated quantitative development.

Develop, enhance, and validate quantitative risk models for derivatives and other financial products. Implement risk analytics solutions on the Atoti platform, analyze pricing and valuation methodologies, investigate exposures and hedging strategies, and collaborate with clients, quants, risk managers, and engineering teams. Translate quantitative requirements into scalable technology solutions while supporting client workshops, product innovation, and risk management analytics across multiple asset classes.

11-week internship performing quantitative research to develop, backtest, and implement automated trading strategies. Translate mathematical models into code, analyze unconventional data sources, conduct statistical research (ML, time-series, NLP), and collaborate with senior researchers to refine trading signals and monetize models.

As a Quantitative Research Analyst Intern, you will model markets, develop algorithms, and conduct statistical analysis to enhance trading strategies.

Supports quantitative trading analysis by analyzing proprietary market data, developing execution signals and impact models, creating visualizations, backtesting trading features, and evaluating algorithm performance. The intern will work with quantitative analysts on execution strategies, market microstructure, statistical analysis, and client-facing research materials. This is a 12-week, New York-based internship requiring advanced Python, pandas, SQL, computational finance, statistics, mathematics, and exposure to machine learning.

As a Quantitative Research Analyst, you will develop and test automated trading strategies using statistical techniques and algorithms, conduct research, and analyze data.

Conduct research, statistical analyses, and develop automated trading strategies using large data sets and sophisticated statistical techniques.

Join the Indexes New Product Development team to design, develop, validate, and launch thematic and smart-beta indexes. Collaborate with research and product teams, build portfolio construction and analytics tools using statistical and machine learning methods, explore AI implementations, and produce white papers, factsheets, and client-facing materials to support product launches.

Supports the design, implementation, testing, and improvement of valuation models for derivatives, structured products, swaps, fixed income, securitisations, and other complex financial instruments. Performs valuation analyses, Monte Carlo simulations, market data validation, output reviews, technical reporting, and client deliverables. The role also contributes to automation of valuation processes and applies quantitative methods from finance, mathematics, statistics, computer science, and economics.

Own the analytical and technical backbone of trading risk and fraud operations. Build production-grade infrastructure, monitoring tools, dashboards, and detection algorithms for toxic flow, latency arbitrage, and abusive trading. Investigate suspicious activity, assess product and promotion risks, classify clients for A-book/B-book decisions, and deliver solutions from data extraction through deployment. Improve automation, identify process gaps, report findings, and mentor junior analysts.

As a Quantitative Research Analyst, you will develop models, conduct complex data analysis, and work with stakeholders to optimize investment strategies.