You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

You will work with Python 3.10+, JavaScript/TypeScript, Go, RabbitMQ, Kafka, PostgreSQL, Redis, OpenTelemetry, Prometheus, Grafana, and Zabbix on Linux servers. You will join a team building and supporting systematic trading models for a sports betting company. You will collaborate with quantitative researchers, developers, and traders.

Hudson River Trading★4.5 (2)· London

You will use Python, C++, Pandas, NumPy, R, or MATLAB to build and maintain predictive trading models. As an algorithm developer, you research and test novel order execution and model training methods, applying

Hudson River Trading★4.5 (2)· London

You will work with Python, Pandas, NumPy, R, and MATLAB to apply statistical analysis and machine learning to vast quantities of market data. The team builds and maintains predictive trading models for the company's high-performance trading infrastructure. You will research, develop, and test order execution methods to increase trading efficiency.

Work with Java (including JDK migration), Kotlin, Python, Kubernetes, ArgoCD, OpenTelemetry, and modern CI/CD pipelines to develop and maintain distributed services for ingesting and processing financial market reference data. You will modernize critical production systems, including a large-scale Java/JDK upgrade programme and improvements to platform reliability and cloud-native infrastructure.

Designing and productionising ML pipelines with Python, Numpy, Scipy, Pytorch, Polars, Ray, Plotly, Dash, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure). The team builds advanced sports betting analytics and trading platforms, turning prototype trading models into production-ready systems while scaling architecture for low latency.

You'll develop systematic HFT and market-making strategies in cryptocurrency markets using Python for research, signal generation, backtesting, and live performance analysis. Working with your own firm's capital, you'll own the full research lifecycle—from hypothesis to production deployment—optimising execution quality, latency, inventory risk and spread capture across global exchanges.

You'll work with C# and .NET as core systems, plus Python across quant and data groups, and may use other OO languages. The role involves designing and building high-impact trading and research platforms for a fixed-income hedge fund's front-office engineering team.

Develop end-to-end solutions in Python (pandas/numpy) for the Commodities RAD team, building data, research & trading platforms and tools for Portfolio Managers. Also use React/Angular for web frontends and SQL for databases.

JPMorgan★3.9 (20k)· London

Lead projects on the Strategic Index Technology team, using Python, Java, C++, or C# to build and maintain the cross-markets index platform alongside Quantitative Research, Trading, and Sales.

You will work with Go and/or Python, plus Kubernetes, Docker, and cloud infrastructure. You'll support a systematic cryptocurrency trading team by maintaining live production trading and research systems, and helping to deploy strategies.

Work with Python (pandas, NumPy) and tree-based machine learning to analyze high-frequency limit orderbook data and build predictive signals for proprietary trading. DV Trading provides liquidity to worldwide markets and hedging opportunities. You’ll collaborate with senior researchers and traders to refine production-ready models.

Durlston Partners· London

You'll use Java, Spring Boot, Kafka, and AWS (EKS, S3), plus PostgreSQL, SQL Server, and CI/CD. The team builds the core trade processing, reconciliation, and reporting platform for a leading systematic hedge fund’s multi-asset trading, collaborating closely with traders and quants.

You will design and build Python services and APIs using AWS (Lambda, S3, ECS/EKS, Step Functions, Aurora), Kafka, and PostgreSQL/SQL Server/Snowflake to support portfolio construction, trading, and analytics. This Investment Technology role turns research tools into production-grade platforms for Portfolio Managers and Quantitative Researchers across Equities, Fixed Income, and Alternatives.

As a Kdb+/Q Engineer at Citadel Securities, you will design and build high-performance data and analytics platforms using Kdb+/Q, Python/Java/C++, and Linux/distributed systems for real-time and historical market data that power trading and quantitative research globally. You will collaborate with traders, researchers, and engineers on scalable solutions for large-scale market data and time-series analytics.

Work with Python, Java, C++, Linux, and kernel-bypass networking. You will develop and maintain infrastructure for low-latency market data delivery and order execution across global exchanges, supporting multiple trading desks at a quantitative investment management firm.

Develop trading algorithm implementations, options pricing, volatility calculations, and simulation frameworks in Java 21+ and Python. You will collaborate with traders, researchers, and quants on high-impact projects for IMC, a global trading firm.

Jump Trading★3.8 (11)· London

As a core development intern, you will design, code and test the firm's distributed trading system using C++ and Python on Linux, with Git for version control, and gain experience with Agile methodologies. The team builds and maintains Jump Trading's world-class low-latency trading platform and infrastructure.

You will work primarily with C# and .NET, alongside Python used by quant and data groups. You will design, build, and enhance high‑impact trading and research platforms for a leading fixed income hedge fund, covering core trading systems and front‑office tooling.

Jupiter Asset Management★4.3 (4)· London

Work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, Azure cloud tools, large language models (LLMs), and Power BI. As a Data Scientist at this investment management firm, you build predictive models, research alternative data sources for alpha signals, and deliver data-driven solutions directly with investment professionals.

You will use Python with Pandas and NumPy to build and manage end-to-end data pipelines, extracting and cleaning fixed income market data for systematic strategies. The role bridges Research, Trading, and Engineering to design and automate datasets for bonds, rates, credit, and derivatives at a global quantitative investment manager.

The Data Scientist will use Python, Pandas, and NumPy to extract, clean, and aggregate data from raw sources, collaborating with Researchers, Traders, and Engineers. They join the Data Search & Analytics team at Qube Research & Technologies, a global quantitative investment manager applying a scientific, data-driven approach across liquid asset classes.

You'll develop high-performance algorithmic trading systems and real-time execution platforms in Java, working with Kafka or Solace messaging technologies and low-latency distributed systems. The Equities Tech Algorithmic Development team at Millennium, a global alternative investment firm, builds execution technology supporting Portfolio Managers across developed and emerging markets. You'll partner with technologists and trading teams to enhance VWAP, TWAP, and other execution algorithms while ensuring performance, scalability, and regulatory compliance.

Point72★3.6 (15)· London

You'll work with large datasets using languages like Python, C++, Java, R, or MATLAB to preprocess data and build predictive models for market dynamics and systematic trading. Point72 is a quantitative investment firm applying advanced statistical learning to market prediction. This internship involves feature engineering and data validation for model estimation across a small team environment.

Point72★3.6 (15)· London

Cubist Systematic Strategies, Point72's affiliate, seeks quantitative researchers to develop statistical and predictive models for systematic trading across equities, futures, and foreign exchange using languages including Python, C++, Java, R, and MATLAB. Researchers will independently conduct rigorous research into market anomalies, managing methodology selection, data collection, backtesting, and performance monitoring. Candidates should have 3-7 years of alpha-driven quantitative research experience and advanced degrees in finance, computer science, mathematics, physics, or related quantitative disciplines.