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

Prima★3.6 (45)· London

You'll work with Kafka, Flink, Spark, Databricks, Python, AWS, Redshift, PostgreSQL, and DevOps/CI-CD tools to build data products and pipelines for Prima's pricing and underwriting domain. The role bridges machine learning and engineering, focusing on data architecture, transformation, governance, and enabling teams to ingest, store, and serve data products. You'll join over 350 engineers across Prima, an online motor insurance provider trusted by 5 million drivers expanding in the UK and Spain.

Design, build, and deploy quantitative models using Python, R, and libraries (numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot) plus NLP tools, SQL, AWS, Sagemaker, DataBricks, Snowflake, MLFlow, and Weights & Biases. The team creates systematic country risk analytics and ML/NLP pipelines for BMI’s market insights, supporting clients in risk assessment.

Work with Python, Pandas, NumPy, Scikit-learn, SQL, LLMs, RAG, and MLOps to design, build, and deploy enterprise-scale AI/ML solutions improving operational efficiency, service resilience, and customer outcomes at LSEG, a global financial markets infrastructure and data business.

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.

LSEG★3.7 (402)· London

You will use Python (Pandas, NumPy, Scikit-learn), SQL, cloud-based MLOps platforms, and Generative AI/LLM/RAG tools to build predictive models and AI solutions that improve operational efficiency, service resilience, and customer outcomes for LSEG's financial markets infrastructure.

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.

Corpay★3.0 (944)· London

You will work with Python, pandas, NumPy, scikit-learn, SQL, LLM/agentic AI/RAG systems, and cloud platforms (AWS, GCP, Azure). You will support the Data Science team in developing predictive models, machine learning workflows, and agentic LLM-based tools for Corpay’s commercial payments business.

Fitch Solutions★3.0 (2)· London

As a Data Scientist at BMI (a Fitch Solutions business), you will design, build, and deploy quantitative models using Python and R (e.g., numpy, pandas, scikit, pytorch) to power advanced analytics. You will develop and maintain ML/NLP and data pipelines, working with Economists and Analysts to build risk models and generate insights from structured and unstructured data.

You will work with Python (NumPy, SciPy), C++, large datasets, distributed systems, and high-performance computing concepts. The team builds high-performance trading and analytics platforms for a HFT leader that transforms sports data into competitive insight, and you will convert prototype models into production systems and optimize for low latency and scalability.

Build high-performance trading and analytics platforms using Python (NumPy, SciPy) and optionally C++, with large datasets, numerical computing, distributed systems, and high-performance computing. The team turns complex sports data into actionable insight for a HFT leader.

Work with Python, Pandas, NumPy, REST APIs, GCP, and MLOps tools on agentic AI, deep learning, NLP, or computer vision projects for Insight International (UK) Ltd.

This Data Scientist role uses Python, SQL, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Power BI, and Tableau for data analysis and machine learning. The position is at Information Tech Consultants, an IT management and consulting firm specializing in data science and IT solutions for UK and international clients.

Work with Python, PyTorch, scipy, scikit-learn, numpy, pandas, MLflow, Docker, Kubernetes, Ray, and GCP to build and scale distributed compute for research workloads. Phaidra creates AI-powered control systems for the industrial sector using reinforcement learning to enable facilities to learn and improve automatically.

WPP Media★4.0 (1)· London

This role uses Google Cloud Platform, Jira/Trello, and statistical methods to analyse media campaigns. You will work within WPP's Data, Tech, and Analytics team to deliver measurement frameworks, brand and conversion lift studies, and campaign effectiveness insights for the global client JLR.

Work with Python, NumPy, pandas, SQL, dbt, Snowflake/Databricks, and LLMs to build and deploy ML models and agentic systems (function calling, multi-agent orchestration) for Planday’s scheduling and workforce management platform. You’ll ship production features on a small, high-impact team.

Barclays★3.9 (6.9k)· London

Lead a data science team at Barclays, working with Python, NumPy, Pandas, scikit-learn, and PyTorch to design, develop, and deploy production-grade AI and machine learning solutions. The role builds scalable, governed AI systems to extract insights from the bank’s data, informing strategic decisions and driving innovation. You will set technical direction and coach a team of data scientists.

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.

You will work with Python, Pandas, NumPy, geospatial libraries (GeoPandas, Rasterio, PostGIS), time-series analysis, and machine learning. The team is building NIRA, an AI-powered ocean intelligence platform that transforms live environmental sensor data into actionable insight for monitoring marine ecosystems.

You will work with Python, OpenAI, Azure OpenAI, LangChain, LangGraph, LangSmith, DeepEval, RAGAS, PromptTools, Docker, Kubernetes, and CI/CD. As part of a multidisciplinary AI engineering team on a major UK retail transformation programme, you will define and implement evaluation frameworks for enterprise-scale LLM applications and agentic AI systems.

Castleton Commodities International★3.2 (24)· London

You will work with Python, SQL, Snowflake, Airflow, pandas, NumPy, SQLAlchemy, cloud platforms (AWS/Azure), and web scraping frameworks. As a Data Engineering Intern, you will build and optimize data pipelines that power analytics, forecasting, and investment decision-making for Castleton Commodities International, a global energy commodities merchant.

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.

Work with Python, PyTorch, and large-scale deep learning (transformers, sequence models, representation learning) to build and deploy models. As a researcher on the Options team at Citadel Securities, you turn petabyte-scale data into trading models with direct impact on options markets.

Capgemini★3.7 (11k)· London

You will work with Python, Scikit-learn, Pandas, NumPy, XGBoost/LightGBM, TensorFlow/PyTorch/Keras, SQL, MLOps (CI/CD), and GenAI tools (LangChain, LLMs, RAG, vector databases). You will design, develop, and deploy machine learning and generative AI solutions for banking and financial services, covering fraud detection, risk modeling, and customer analytics.