You'll work with Python, PostgreSQL, MySQL, BigQuery, FastAPI, Flask, GCP/AWS, and CI/CD pipelines. You'll join the commercial performance insights squad to build data pipelines and event streams for monitoring algorithmic insurance underwriting, including Monte Carlo simulation.

You will work with Java microservices, Spring Boot, Python, SQL, Snowflake, dbt, Airflow, LangChain, Sagemaker, and MLflow. The data science team builds ML solutions for risk-based decisioning, pricing, and operational automation in insurtech/fintech. This is a hands-on leadership role—you will lead and develop the team while setting technical standards.

Work with Python, SQL, Git, Docker, cloud platforms, and CI/CD to develop, test, and deploy software and AI solutions, build data processing pipelines, and contribute to machine learning and automation projects. The team creates AI projects that drive real-world solutions.

Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

Work with Python, SQL, SHAP, Airflow, PyFlink, BentoML, Git, Jenkins, Octopus, Docker, JIRA and MLFlow. As a Data Scientist at Zempler Bank, you develop algorithms to protect customers, widen credit access and improve financial outcomes. You implement and monitor models in production.

Escalent★3.6 (38)· London

You’ll work with Python and frameworks like Django, Flask, or FastAPI, building RESTful APIs, microservices, and data pipelines while integrating AI/ML models and LLM APIs. Tech includes SQL/NoSQL databases, AWS/Azure, Docker, Kubernetes, and CI/CD. Escalent is a data analytics and advisory firm, and you’ll develop AI-powered applications and intelligent workflows in its cloud-native environment.

You will work with kdb+, q, Python or Java, APIs (REST, messaging), Linux, AWS/GCP, Docker/Podman, and CI/CD. You will develop, test and maintain kdb+ applications and supporting services for market data and analytics on a Client Engineering team for a FinTech leader. You will also mentor junior developers.

You will develop and deploy LLM-powered applications using PyTorch, SGLang, and PEFT/LoRA, building and fine-tuning embedding and reranking models. You will work on an early-stage platform that combines machine learning with real-world trading workflows to transform specialised financial markets. You will join a small engineering team with direct collaboration alongside founders and traders.

Senior Software Developer role in the Data team of a global energy trading house, working on data-consumption and publishing platforms using C#, .NET, ASP.NET, Angular, SQL Server, Oracle, Docker, Grafana, Kafka, Azure Service Bus, and Azure DevOps. Responsible for end-to-end delivery, mentoring, and architectural direction.

Capgemini★3.7 (11k)· London

Work with LLMs, vector databases, prompt engineering, multi-agent frameworks, and AWS (SageMaker, ECS, Fargate, Docker, Kubernetes) to design reusable architecture patterns and integrate GenAI into enterprise applications, APIs, and data ecosystems. The role builds AI-powered solutions for Capgemini, an AI-powered global business and technology transformation partner.

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.

Wise★3.5 (42)· London

You will build scalable systems for business onboarding and verification using Java or JVM languages, Spring framework, JDBC, and relational/non-relational databases with CI/CD pipelines and distributed systems. This engineering team owns end-to-end projects to automate verification, create flexible onboarding journeys, and manage compliance risks for Wise’s money-moving platform across Europe and the UK.

You'll build scalable AI data pipelines, cloud-native backend systems, and microservices using Python, FastAPI/Flask/Django, SQL, AWS/Azure, Docker, and Kubernetes. The company specialises in transforming AI research into secure, scalable production systems. You will also mentor junior engineers and contribute to architecture decisions.

Work with Python, PyTorch, JAX, CUDA, Docker, Kubernetes, Slurm, Ray, and Linux on GPU computing. The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery, including biophysics, chemistry, cofolding models, simulation workflows, and agent-usable scientific pipelines.

Build and deploy AI agents for geopolitical and financial risk intelligence, working with Python, SQL, RAG pipelines, BM25, vector/hybrid retrieval, APIs, Docker, Kubernetes, GitHub Actions, and AWS services like RDS, EKS, and S3. Kharon provides risk-analysis tools for global financial institutions and multinationals, covering sanctions, export controls, and threat identification.

Work with Python, SQL, graph databases, graph ML, Docker, Kubernetes, and LLMs. Own and evolve Kharon's risk propagation engine that analyzes entities in a knowledge graph to surface risk signals for global security clients.

Chubb Insurance★3.7 (1.3k)· London

You will use Python, Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, TensorFlow, Docker, and cloud platforms to build and deploy LLM, RAG, and conversational AI applications. As part of Chubb Insurance's Global Analytics team, you'll design production-ready AI solutions for business value.

You'll work with Java/JVM and Spring on the backend, React/JavaScript/TypeScript on the frontend, plus CI/CD, distributed systems, and relational/non-relational databases. You'll join Wise's Account Integrity team, building fraud-prevention and financial-crime detection systems to protect customer accounts. Wise is a global technology company for moving and managing money.

Tesco★3.5 (25k)· London

You will work with Java, Spring Boot, Kafka, Apache Flink, Apache Beam, NoSQL databases, Redis, and Apache Camel. The Tesco Personalisation team builds real-time, low-latency services for recommendations, offers, and experimentation across web, mobile, and in-store channels using streaming data and ML.

This senior software engineer role uses Python, Snowflake, dbt, Kafka, Java, AWS, and Linux/Docker. You will build ELT pipelines and a streaming platform for tick data, consolidating legacy systems as part of the data engineering team for a systematic hedge fund.

You'll work with Python, SQL, PySpark, and the PyData stack to build machine learning and analytics solutions for McKinsey's clients across industries, translating business challenges into statistical models and deploying them to production. QuantumBlack, AI by McKinsey is a data science and AI consulting firm applying advanced analytics to solve complex client problems. You'll collaborate with Data Scientists, Engineers, Designers, and Product Managers globally while based in London, with exposure to R&D projects and conference presentations.

Work with Microsoft Fabric (Lakehouses, Warehouses, Dataflows, Pipelines), Azure (SQL, Data Lake, Data Factory), SQL, Python, and REST API/SFTP to build ETL/ELT solutions. You will deliver modern data integration solutions for a diverse portfolio of clients within RSM’s Global VAT Team.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.

You will build AI agents using Python, SQL, Docker, Kubernetes, AWS (RDS, EKS, S3), GitHub Actions, RAG pipelines, BM25, vector search, hybrid retrieval, and knowledge graphs. These agents interpret geopolitical and financial signals for Kharon, a company providing global security risk intelligence.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.