Top Python Jobs in London

Design, build and maintain production-grade Tableau dashboards and reports for global FX stakeholders. Curate, cleanse and validate underlying datasets using SQL, Python and KDB/q. Implement data governance, quality controls, automated testing and CI/CD for reporting. Contribute Gen AI integrations (NL2SQL, automated commentary), promote data-as-product practices, and shape FX data and reporting strategy.

Perform data collection, analysis, and interpretation to identify trends. Build and maintain dashboards and reports, ensure data accuracy through quality checks, collaborate with teams to deliver actionable insights, and support development of analysis methodologies.

Build and operate Kafka and CDC pipelines, help architect an Iceberg-backed Lakehouse, deliver scalable data serving layers, enable self-service analytics, drive data observability, and consult teams on integration, modelling, and schema design.

Design, develop and maintain ETL/data ingestion pipelines, profile and clean large-scale data, implement data quality procedures, support data management standards and anonymization, and contribute to analytics and visualization using cloud platforms and open-source stacks.

Design, deploy, and operate production ML models (Churn, Next Best Action), build customer segmentation and xLTV models, productionise models with Data Engineering, monitor for drift, optimise latency, run A/B tests, and enable GenAI/RAG applications and stakeholder translation.

Lead delivery of advanced analytics and data science for global clients, develop scalable AI-enabled analytics products (prediction engines, experimentation, MMM), embed analytics into client decision-making, and build capability across teams through playbooks and thought leadership.

Design, build and deploy production-grade ML systems and infrastructure. Create reusable, scalable solutions, lead technical scoping and architecture, collaborate with data scientists and engineers, and advise clients and stakeholders on ML feasibility, standards, and delivery.

Provide DevOps/SRE support across multi-cloud (Azure, GCP, AWS): manage infrastructure, CI/CD pipelines, automation, WAF/CDN, incident response, performance and availability, and remediate security vulnerabilities.

Lead delivery of advanced analytics and AI solutions for global clients, design scalable analytics products, industrialise prototypes to production, embed measurement and experimentation, drive adoption across markets, and build analytics capability and thought leadership within the organisation.

Lead a seven-person BI team to deliver trusted reporting, maintain dbt models and Tableau dashboards, drive data quality and governance, manage stakeholder prioritisation, and define a strategic BI roadmap including semantic layer and AI-enabled analytics.

Develop, fine-tune, deploy, and monitor generative AI models; contribute to prompt engineering, RAG and hybrid search workflows; perform data preprocessing, feature engineering, API development; collaborate on integration, testing, code reviews, documentation, and ensure AI compliance and responsible-AI practices.

Customer-facing, hands-on engineer who designs, builds, and productionizes end-to-end data and AI solutions on Databricks. Leads architecture, implements data pipelines and ML integrations, delivers measurable outcomes, and creates reusable assets while working with cross-functional teams and enterprise customers.

Customer-facing, hands-on engineer who architects, builds, and productionizes end-to-end data and AI solutions on the Databricks platform. Leads design decisions, implements data pipelines, ML/AI integrations, and user-facing applications, partners with cross-functional teams, delivers projects to specification, and creates reusable accelerators to scale customer impact.

Own, design, build and maintain backend services for neobanking features (transfers, open banking, savings, payments). Lead end-to-end delivery, migrations, observability, incident response, architecture and code quality. Collaborate with product, design, data, security and operations to ship reliable, regulated fintech features.

As a Customer Success Engineer, you will ensure customer adoption of Algolia's search platform through technical onboarding, solution design, and ongoing support, while collaborating with customers and internal teams to drive technical excellence.

The role involves analyzing trading activity, developing models for pricing and risk management, statistical analysis, and collaboration with traders and technology teams.

Design and implement end-to-end pricing, market-making, and risk models for prediction markets. Build data pipelines, research cross-market dependencies and combinatorial products, partner with traders, and develop backtesting, simulation, monitoring, and validation tooling to support live trading and model calibration.

Support day-to-day compliance operations for physical and financial commodities trading across Europe. Maintain and enhance monitoring dashboards, reports and data checks using Python, SQL, Power BI and Excel. Assist with regulatory analysis, audits, data requests and ad-hoc projects while collaborating with trading, operations, technology, risk and other stakeholders to enable compliant trading and investing activities.

The AI Enablement Engineer leads programs for engineering teams to integrate AI, teaches best practices, and develops scalable learning models.

The Staff Machine Learning Scientist will design, develop, and maintain machine learning models for Depop's fashion resale platform, leading technical initiatives and collaborating across teams to drive innovation in ML solutions.

Lead the design and development of AI solutions for clients, implementing language models and frameworks, mentoring engineers, and evolving internal AI platforms.

Lead the design and development of AI-powered applications, mentor engineers, and collaborate across teams while ensuring high coding standards and deployment in cloud environments.

The Principal AI Engineer will define AI architecture, lead teams, develop solutions, and mentor engineers to enhance enterprise AI systems.

The AI Research Engineer will research and develop novel model architectures, adapt open-source models, and contribute to the technical strategy and culture of the team.

The role involves developing new AI model architectures and adapting existing models, conducting experiments, and contributing to technical strategy in a high-growth research environment.