Cint★3.5 (13)· London

Use Python and SQL for statistical modeling and machine learning to optimize Cint's programmatic research marketplace (Cint Exchange). You will analyze marketplace dynamics, conduct A/B tests, and build predictive models to guide product and pricing strategy.

You will use SQL, Python, Azure, and Snowflake to design and maintain data pipelines, analytical datasets, and calculation logic. This role supports a global marketing advisory firm by building the data foundations that underpin audience and marketing intelligence offerings for clients.

You will work with Snowflake, DBT, SQL, GitHub Enterprise, market data platforms and AI-assisted tooling such as GitHub Copilot and Snowflake Cortex AI. You will design, build and optimise scalable data solutions and data models for a financial services programme transforming investment decision-making and regulatory reporting.

Analytics Engineer role using SQL, dbt, Databricks, Kafka, Databricks Workflows, and data activation tools like Hightouch. You'll build data products and pipelines supporting ClearScore's 26 million users in making financial decisions, serving the operational platform and CRM.

You'll work with time-series data, supervised and unsupervised ML, CI/CD, version control, and tools like Claude Code, MLflow, and cloud platforms (Azure, AWS, GCP) with MongoDB. Ceto provides operational intelligence for maritime, analyzing ship data to predict mechanical failures through anomaly detection and degradation forecasting.

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

Goldman Sachs★3.9 (2.3k)· London

Build a high-performance, low-latency data platform for post-trade processing using Java, Kafka, Flink, SingleStore, HDFS/S3, and Scala. You will own the full lifecycle of core systems, designing distributed computation and an AI-powered UI/agent framework to deliver intelligent analytics over large-scale business data.

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.

Design and maintain scalable data integrations using Azure Logic Apps, Storage Accounts, API Management, Microsoft Fabric, and Azure DevOps for CI/CD. You will lead integration pipelines that enable data movement across Bell, ensuring governance and reliability. The role also includes mentoring junior team members.

You will use SQL, Python, BI tools (PowerBI, Looker, Tableau, etc.), and spreadsheets. As a Data Analyst on the Fraud team at Moniepoint, an all

This Data Analyst role involves performing ad-hoc data extractions using SQL and supporting digital analytics with Google Analytics and Tag Manager. Transform is an end-to-end transformation partner that builds and operates systems for clients in the public sector and dynamic brands.

Design, develop and optimise scalable data solutions using Azure Databricks, Azure Data Factory and Scala. This role processes large-scale datasets, builds ETL pipelines and works with stakeholders within a London-based long-term data engineering programme.

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.

Uses Python, SQL, Pandas, NumPy, SciPy, Scikit-learn, PyTorch/TensorFlow, Git, Linux, and cloud computing. Researches and develops alpha signals, systematic strategies, and portfolio construction for global equity markets at a systematic trading firm.

A 12-month contract role in Waterloo requiring active SC clearance. You will design and deploy scalable ML algorithms, work with time-series data, and use Python, JavaScript, Java, React, and cloud platforms for an enterprise AI client.

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.

You will work with Databricks, Python, SQL, Kafka, and Azure to build scalable batch and streaming ingestion frameworks. This role supports the modernisation of a large-scale enterprise data platform for a global Energy & Commodities Trading organisation, migrating legacy pipelines onto a modern data architecture.

You'll use Python, machine learning, statistical methods, Apache Spark, AWS, Docker/Kubernetes, CI/CD, and Linux to build pipelines and models. The role supports a national security consultancy in London, developing mission-critical data and AI solutions for UK Government clients. No team scope is stated.

dunnhumby★3.9 (87)· London

You will apply machine learning (regularised regression, clustering, tree-based ensembles, neural networks, NLP, graph-based approaches) and generative AI using Python, Spark, Hadoop, SQL, Pandas, and scikit-learn. The role focuses on insight automation and product assortment for retail clients. You will work within Applied and Research Data Science teams.

You will use Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, and Azure cloud tools to build predictive models and develop reproducible data science processes alongside a team of investment professionals at a UK investment management company.

You will own the dbt project (models, tests, CI/CD) and work with BigQuery (or Snowflake/Redshift), advanced SQL, and a BI tool like Looker or Metabase. As an Analytics Engineer for a £100m+ D2C e-commerce brand, you'll transform raw data into trusted dashboards that drive marketing, finance, and operational decisions.

FDJ UNITED★4.0 (96)· London

Build and maintain batch and streaming data pipelines on AWS using dbt, Spark, SQL, Kafka/Flink, and Airflow. Work on the Sportsbook Data Platform, a modern event-driven lakehouse transforming sportsbook data (bets, offers, etc.) into curated datasets for analytics, trading insight, and machine learning.

FDJ UNITED★4.0 (96)· London

You'll lead a DataOps/MLOps team building an ML platform and data operations capability for FDJ United's Sportsbook, working with AWS, Terraform/CloudFormation, Spark, Kafka, and CI/CD pipelines for end-to-end ML lifecycle management. The role involves establishing best practices, monitoring, and governance while collaborating with Data Engineering, ML Engineering, Quants, and Data Science teams to personalize customer experiences and manage risk.

You will work with dbt, Snowflake, Databricks, Python, SQL, and AWS services including S3, Glue, Lambda, Step Functions and MWAA. As a Data Engineer, you will design, build and deliver modern cloud data platforms, scalable ETL/ELT pipelines and analytics solutions for enterprise clients.

You will use Python, SQL, dbt, Snowflake, Hex, Omni, and Looker to build pipelines, dashboards, and evaluation frameworks for analysing unstructured conversation data (prompts, model outputs, user edits, feedback). You will join the Product Data team at Synthesia, the world’s leading AI video platform for business, to discover user-behaviour patterns that directly shape product improvements. If hired at the Principal level, you would set the broader data science agenda and mentor the existing analytics team.