Quantitative researchers will develop mathematical, statistical, and machine-learning models for systematic trading, working in Python, with C++ and SQL as desirable, alongside large-scale data analysis. The role spans full research-to-production, collaborating with portfolio managers and engineers at a global quantitative investment firm. No team size is stated.

You will use Python, SQL, and Streamlit on an AWS data platform to clean survey data, build statistical and machine learning models (including NLP), and design interactive dashboards. As a Data Scientist II on the Data Science Consulting team, you will deliver predictive analytics solutions that optimise clients’ marketing effectiveness. You will also manage client relationships and guide more junior team members.

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.

Python, C++, KDB+/q, Git, Agile, CI/CD, and database technologies. You will partner with Quantitative Researchers to build scalable Python applications and high-performance data processing for research, analytics, and live trading at a hedge fund.

You will work with C++, Python, and Rust to build high-performance, low-latency trading systems, market connectivity, and large-scale data platforms at a quantitative trading firm where engineering drives trading performance.

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.

Python (4+ yrs), Git, Agile, and optionally C++, KDB+/q, CI/CD, and database technologies. You will build scalable applications, workflows, and high-performance data-processing tools for research and live trading alongside Quantitative Researchers at a hedge fund.

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.

Hudson River Trading★4.5 (2)· London

You will write and maintain critical trading infrastructure using C/C++ or Python on Linux, working closely with engineers who design trading algorithms in a fast-paced, real-time environment. The role is with Hudson River Trading, a firm that brings a scientific approach to trading financial products.

Hudson River Trading★4.5 (2)· London

Software engineering interns work with Python, C, or C++ on independent programming projects and gain hands-on exposure to HRT's algorithmic trading research and infrastructure.

Work with advanced Python, large complex datasets, and a proprietary technology stack including high-performance trading systems and data platforms. As a Quantitative Researcher, you will lead independent research to discover and deploy alpha-generating systematic investment strategies across global equity markets.

You will work with Python, C++, streaming platforms, SQL, and Linux to build scalable risk data pipelines and ETL solutions. The team develops the data infrastructure that delivers real-time and end-of-day risk data for a quantitative investment firm’s systematic trading strategies.

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.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments, working with Python, petabytes of data, and a large GPU cluster. They apply statistical and ML techniques, from linear models to deep learning.

Founding Engineers own the full stack: Python, C++/Rust (bonus), and production data pipelines. They build risk models, backtesting infrastructure, scenario tools, and an AI-driven research layer on massive financial datasets. The team owns entire systems—from math and data to APIs and interfaces—for a live fintech serving professional investors.

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 will work with Python and SQL, designing and developing research and live trading infrastructure for systematic, computer-driven trading strategies across equities, futures, and foreign exchange. You will build components for both live trading and simulations as part of a trading pod.

You'll use Python, databases, APIs, and microservices architecture while building and maintaining tools to support trading risk analysis and reporting, processing large volumes of market and trade data across fixed income products.

You will work with Python, C++, HPC, and distributed systems to build tools for quant research, signal generation, and large-scale data pipelines. The team develops the infrastructure powering trading signal research, letting researchers and traders test and scale strategies across global markets.

You'll use modern C++ (Python a plus) to develop an ultra-low-latency trading engine, research platform, and cloud analytics infrastructure for a systematic quant hedge fund. Working directly on a trading desk with traders and researchers, you'll own projects end-to-end and tackle hard technical challenges critical to desk success.

You will work with C++, Python, Linux, machine-learning libraries, Google Cloud, Airflow, InfluxDB, and Grafana. This role builds data engineering and prediction tools for systematic equity trading, developing software for quantitative research, maintaining data pipelines, and implementing large-scale computation and serialization frameworks.

You’ll work with modern C++, Python, and systems-level knowledge of CPU, memory, OS, and networking. As part of a trading-desk Quant Dev team collaborating with researchers and traders, you’ll build ultra-low-latency trading engines, research platforms handling terabytes of daily data, and cloud-based analytics infrastructure for a global systematic hedge fund.

The role involves developing and maintaining in-house C++ pricing libraries, multi-threaded servers, and automated trading systems, while advancing the quantitative toolbox with new algorithms. You will work on model frameworks, signal research, execution and portfolio construction tools, using Python and C++ with QuantLib experience preferred.

Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to build scalable ETL pipelines, financial data pipelines, and cloud-native infrastructure. This front-office role is at a systematic equity hedge fund modernising its quantitative research platform, working directly with portfolio managers and researchers. Experience with S&P Xpressfeed, Snowflake, Bloomberg, MSCI Barra, Databricks, and LLMs is advantageous.