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.

You'll work with Python (Pandas, Arrow, Spark) and potentially Lakehouse tools (Delta Lake, Databricks, AWS). Joining Global Data Engineering, you provide first-line support for a high-frequency trading market maker, ensuring robust data pipelines and APIs for trading teams.

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 work with C++ (including memory management) and Python (Pandas, NumPy) to build high-performance infrastructure, data pipelines, and quantitative trading systems. The team develops and optimizes simulation and backtesting tools and handles high-volume market data feeds.

Python (pandas), SQL, and modern data tools (cloud storage, workflow orchestration, messaging systems) are used. This role builds scalable data pipelines and infrastructure for a multi-asset hedge fund’s front-office trading, research, and portfolio analytics.

Develop Python-based backend services and data pipelines for trading and risk management in investment banking, using Pandas, NumPy, SQL, Flask/FastAPI, and enterprise platforms like Quartz or SecDB. Work with large financial datasets to support risk, PnL, and trade lifecycle workflows, collaborating with quants, traders, and risk teams.

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.

Castleton Commodities International★3.2 (24)· London

You will use Python (Pandas, NumPy) to apply machine learning models (ARIMA/SARIMA, XGBoost, LSTM, linear regression) for time series forecasting on power, natural gas, and oil market fundamentals. This supports trading and risk management decisions at Castleton Commodities International, a global energy commodities merchant. You will collaborate with Data Scientists, Analysts, and Traders.

Castleton Commodities International★3.2 (24)· London

Work with Python, AWS (including Kubernetes and Kafka), Snowflake, APIs, Streamlit, Git, Docker, Helm, and CI/CD pipelines to build front office software tools and microservices. The internship contributes to a leading-edge analytics platform that supports real-time trading, market data analytics, and investment workflows at Castleton Commodities International, a global energy commodities merchant.

Work with Python, pandas, NumPy, SQL and time-series databases (e.g. kdb+, InfluxDB, TimescaleDB) along with cloud platforms (AWS, Azure, GCP). The role builds and optimises data infrastructure—scalable architectures, pipelines and querying—for the Global Oil desk, supporting investment decisions and trading strategies.

Jump Trading★3.8 (11)· London

A 10-week internship working with Python on Linux, building and maintaining data pipelines and infrastructure for Jump Trading's proprietary trading platform, using Agile development and production code.

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.

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 work with Python, TypeScript, AWS (Cognito, Lambda, Fargate, API Gateway, S3, IAM), Terraform, PostgreSQL, and AI tools (Claude, GitHub Copilot, Cursor, LLM APIs). The team builds and evolves scalable microservices for BMLL Lab & Vantage, the company’s SaaS products for data science and visualisation on large financial datasets.

Work with Python, Pandas, and Polars to build data pipelines for extracting, cleaning, and aggregating financial market data. As part of a data-focused function, you support quantitative researchers and traders at a global systematic investment manager by designing datasets that drive systematic strategies and trading decisions.

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.

You will work with Python (pandas, numpy), SQL, Snowflake, and AWS to design and implement solutions for sourcing, storing, and delivering end-of-day market data. This role supports the Risk Technology group's global platform for measuring and analyzing market and credit risk exposures.

You'll work with Snowflake, AWS, Python (pandas, numpy), SQL, and Quant APIs to build and enhance the market risk time series infrastructure. The Risk Technology group supports a global risk platform that enables the Risk Management group to oversee market and credit risk exposures.

Deutsche Bank★3.8 (3.5k)· London

You'll work with Python, Scikit-learn, Pandas, and Numpy to develop anomaly detection models and fraud identification controls across Deutsche Bank's Investment and Corporate Bank. The TaCC team within Group Strategic Analytics builds bespoke models with global responsibility for model development, data integration, ETL/ELT processes, and production deployment across all products, businesses, and regions. You'll own problem sets, manage stakeholders, and drive growth of the codebase.

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.

Citi★3.9 (21k)· London

You will build trading analytics and desk tooling using Python (Pandas, FastAPI, Airflow, Streamlit), high-performance data stores (Trino, Snowflake), and agentic AI frameworks for Citi's Equities Technology team supporting Derivatives Flow, Delta 1, and Exotics desks. The role involves creating real-time pricing, risk analytics, backtesting frameworks, and data pipelines for traders and quants across execution, prime clearing, and post-trade platforms during a multi-year technology transformation.