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.

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

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.

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.

You will use Python or C++ (with pandas, numpy, numba, multiprocessing, or GPU workflows) to design and refine systematic trading strategies, build volatility prediction and regime detection models, and run large-scale simulations. The team builds advanced algorithmic trading systems across FX and crypto, combining dynamic hedging, multi-agent grid logic, and high-frequency data insights.

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, Pandas, and NumPy to prototype, design, and automate data pipelines for extracting, cleaning, and aggregating index and ETF data. The team collaborates with Quantitative Researchers and Traders to design and maintain datasets that drive systematic strategies for a global quantitative investment manager.

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.

Qube Research & Technologies, a global quantitative investment manager, seeks PhD final-year students or postdocs to develop trading algorithms and predictive signals across high, mid, and low-frequency systematic trading systems. You'll work with C++, Python, R, MATLAB, or C# to analyze vast datasets, translate theoretical models into production-ready research, and collaborate with experienced quantitative researchers in a technology-driven environment covering all liquid asset classes.

You'll develop predictive signals using statistical analysis, machine learning, NLP, and AI techniques on large datasets, working with Python, R, Matlab, or C++/C#. You'll lead the full research cycle from signal generation through implementation within QRT's global execution platform, monitoring model performance while collaborating with researchers, technologists, and traders across the organization.

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.

Jump Trading★3.8 (11)· London

You will work with C++, Python, and R/MATLAB in a Linux environment to develop statistical and machine learning models for profitable trading strategies. The quantitative trading team analyzes large datasets across global markets, identifying patterns to forecast price movements and build predictive models used in live trading. You'll collaborate with traders, researchers, and engineers to apply your work to Jump Trading's proprietary trading operations.

LSEG★3.7 (402)· London

FTSE Russell seeks a Quantitative Engineer to build scalable applications supporting index calculations, back-testing, and analytics using Python, SQL, numpy, pandas, scipy, and cloud-native solutions with RESTful APIs and CI/CD pipelines. The role combines software engineering with quantitative finance to develop tools for index monitoring, validation, and rebalancing across equity, fixed-income, currencies, and commodities. Requires 2+ years in quantitative analytics within financial services and an advanced degree in Mathematics, Computer Science, Financial Engineering, Statistics, or Physics.

Tower Research Capital★4.1 (12)· London

You will develop Python tools and improve simulation/backtest frameworks for quantitative trading research, working with statistical libraries like NumPy, Pandas, and Polars. The role supports a trading team within Tower Research Capital, a high-frequency proprietary trading firm, iterating closely with on-site quantitative researchers to enhance trading strategy performance. Experience with C++, Bash scripting, and financial markets knowledge is preferred but not required.

Flow Traders, a proprietary trading firm, seeks a Senior Research Engineer to lead development of their trading model research framework using Python, PyTorch, Polars, Pandas, Ray, AWS/Azure/GCP, Docker, and Kubernetes. You'll design end-to-end ML pipelines for training and deploying trading models in production, then partner with Quantitative Researchers to develop data-driven systematic trading strategies and alpha signals.

You'll build cloud-native, high-throughput distributed systems for real-time market data processing using technologies like Kubernetes, Kafka, and modern data tools (Polars, Arrow, Flight/gRPC), with optional C++/Rust expertise. The Tick Data Platform team, newly formed and small, creates globally distributed market data architecture supporting QRT's quantitative research and trading operations. You'll collaborate with researchers and data scientists to architect scalable platforms from the ground up.

Hudson River Trading★4.5 (2)· London

You'll work with Python, Pandas, NumPy/SciPy, and network protocol analysis tools to optimize Hudson River Trading's hardware-based trading infrastructure across traditional and crypto exchanges. The Market Structure Analysis team analyzes time-series network data, conducts latency-reduction experiments, and improves fill rates by understanding exchange protocols and market microstructure. The role involves data visualization, metrics automation, and cross-team communication to enhance trading technology performance globally.