Top Python Jobs in London

Maintain and optimize cloud infrastructure for scalability and performance, build monitoring/alerts, participate in incident response and RCA, automate operations, tune databases and Kubernetes, create disaster recovery plans and documentation, and collaborate cross-functionally to improve reliability.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, diagnose anomalies, optimize spend, and translate data into clear recommendations. Collaborate with Data and Analytics Engineers to shape data models and pipelines, and communicate insights to non-technical stakeholders.

Lead technical delivery of advanced statistical and ML models for energy planning and forecasting. Hands-on development in Python, time series modelling, MLOps and Azure/Databricks; mentor teams, manage stakeholders, ensure model governance, production deployment and scalable data pipelines.

Lead pre-sales and delivery of application modernization engagements, engaging customers to decompose legacy RDBMS apps, design modern services on MongoDB, run technical workshops, deploy complex demo environments, and advise sales on account strategy. Travel up to 25% and collaborate with engineering and business stakeholders to drive modernization projects.

Partner with business stakeholders to build, maintain, and evolve workload and staffing capacity forecast models. Perform advanced data analysis and modeling, generate actionable insights, administer contact center systems, and communicate findings to support timely staffing decisions and operational excellence.

Design, build, and deploy production-grade generative AI and multi-agent systems for financial services. Architect scalable MLOps pipelines, optimize LLMs and multi-modal models, implement RAG and prompt-engineered agent workflows, and collaborate with clients and cross-functional teams to accelerate GenAI adoption and deliver measurable business impact.

Own and monitor intraday and end-of-day risk exposures for multi-asset prime brokerage clients; develop margin, risk, and stress-testing methodologies; generate and automate risk reporting; engage with clients and senior management on onboarding and risk issues; and collaborate with engineering and product to enhance risk and margin platforms.

Design, build, and operate enterprise-grade, cloud-native data platforms and pipelines for Preqin's Company Intelligence. Provide technical architecture, end-to-end delivery, data governance, observability, and reliability while leading and mentoring a small team and partnering with product and analytics to deliver measurable business value.

Lead end-to-end strategic technical engagements for enterprise customers, shape SOWs, design scalable Klaviyo and martech solutions, manage delivery and risks, advise stakeholders, mentor peers, and drive repeatable processes to ensure adoption and measurable outcomes.

Validate and benchmark ML models on Graphcore's software/hardware stack. Create automated benchmarking pipelines, run open-source models, design targeted tests for numerical precision, quantisation, attention, distributed execution, and debug performance and correctness issues across frameworks and environments.

Design and build test infrastructure, automation, and tooling to validate ML software stacks and hardware; shape test architecture across CI pipelines, frameworks, simulators, emulators, and large ML workloads; mentor engineers and own complex validation projects.

Validate and benchmark ML models and stacks on Graphcore hardware. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, distributed execution and subgraphs, and debug performance and correctness across frameworks and execution environments.

Lead the AML Risk data science team to design, build, and deploy ML systems (unsupervised, semi-supervised, supervised, GenAI) for global AML detection. Hire and mentor specialists, develop detection controls, performance testing, decisioning layers, operational integrations, and maintain production-grade Python services and automated model pipelines.

The Analytics Manager will lead marketing measurement strategy, manage a team of analysts, and optimize marketing effectiveness through data analysis and collaboration.

As a Lead AI Engineer, you'll architect and deploy generative AI systems, optimize large models, and lead teams in AI engineering across financial services.

Support People team with headcount and cost analysis, build dashboards, update hiring forecasts and workforce plans, run scenario modelling, automate reporting, and prepare executive-level presentations to inform strategic decisions.

Lead ML Engineer to build ML experimentation tooling and label quality for Fincrime/KYC/Support squads. Own roadmap, stakeholder management, delivery, and onboarding. Work across MLOps, distributed data processing, Terraform/AWS infra, CI/CD, monitoring, and ML governance; mentor junior team members and run PoCs to production.

Build scalable, reliable backend services for global shared-finance products (international joint accounts, kid accounts). Design asynchronous microservice systems, collaborate with product/design/research, influence architecture, ensure scalability, reliability, and compliance, and deliver customer-facing features used by millions.

Lead analytics for the Financial Crime Platform, partner with product and engineering to build metrics, pipelines, visualisations, and features for fraud/AML risk models, drive discovery and data-driven decisions balancing risk and customer experience.

Build and maintain SOAR playbooks, scripted solutions, and AI-powered workflows to automate detection, triage, investigation, and response. Develop PowerShell/Python scripts, integrate REST APIs and SIEM queries, validate automation, monitor performance, and collaborate with SOC/engineering teams to improve analyst efficiency.

Design, deploy, and maintain tools and services for build, CI/CD, packaging, and releases. Manage cloud and Kubernetes infrastructure, use IaC, reduce toil, and support junior engineers for reliable ML software delivery.

Develop and maintain internal build, test, and integration tooling for the ML software stack. Build release processes, assist dependency management, guide developers on build best practices, collaborate with QA and engineers to resolve integration issues, and contribute fixes to the ML software stack.

Lead analytics for customer operations: build capacity planning and forecasting models, own data pipelines, develop predictive and causal inference analyses, track KPIs, standardise processes, and partner with operations, WFM, quality, and training to drive data-driven improvements.

Senior Data Scientist embedded in international teams to ingest, validate, and transform partner oncology data into research-grade datasets. Build and automate scalable pipelines, apply ML/LLM techniques to unstructured EHR data, and collaborate with cross-functional stakeholders to develop and deploy real-world data products.

Partner with trading, risk, operations, data and technology stakeholders to elicit and document requirements, define current and future-state operating models, produce user stories and functional specs, support Agile delivery (Azure DevOps), ensure traceability, and support testing, UAT and implementation for energy trading transformation programmes.