Top Python Jobs in London

Lead end-to-end data science and machine learning across membership, marketing, digital, operations, food and beverage, and events. Build and productionize churn, recommendation, segmentation, and demand forecasting models; establish MLOps, monitoring, governance, and measurement standards; and partner with stakeholders to prioritize high-value work. Coach analysts, review offshore data science work, and explain complex methods and business impact to senior leaders.

Twelve-week machine learning internship focused on implementing research papers, building custom libraries for petabyte-scale model training, optimizing ML operations, profiling architectures, and evaluating emerging ML hardware and software. Interns will collaborate with quantitative researchers and engineers on advanced machine learning and scientific computing challenges.

Build AI proofs of concept and production-ready solutions for clients, using Python and AI coding tools. Lead technical discovery, testing, security, monitoring, cost control, DevSecOps, and FinOps practices. Coordinate distributed partner and internal delivery teams, review technical work, manage risks and budgets, and explain solutions to non-technical stakeholders. Support proposals while ensuring responsible AI, data protection, and secure handling of client information.

Supports daily loan funding controls, payment reconciliation, refunds, payment files, finance help-desk queries, and partner indemnity recovery. The role verifies partner funding and bank details, maintains audit trails, investigates exceptions, validates recoverable amounts, tracks invoices and shortfalls, and helps automate manual controls. It is a hands-on six-month graduate position requiring strong numeracy, attention to detail, spreadsheet skills, clear communication, and responsible handling of sensitive financial data.

Design and deliver computer vision solutions using Ultralytics YOLO, including model training, export, quantization, integration, and deployment. Lead partner-facing technical discovery, scope projects, build demos and prototypes, review GitHub contributions, and create technical documentation. Collaborate with partnerships, product, and engineering teams while supporting deployments across platforms and representing Ultralytics in meetings and events.

Lead the hands-on development and operation of secure CI/CD pipelines, platform automation, infrastructure-as-code, Kubernetes environments, and software supply-chain security controls. The role drives progressive delivery, observability, reliability, compliance automation, incident response, and disaster recovery. It also provides technical leadership, mentoring, code and design reviews, product-team onboarding, documentation, and enablement for DevSecOps and platform engineering practices.

Designs and develops scalable data platforms, pipelines, ETL/ELT solutions, data lakes, lakehouses, warehouses, and migration initiatives using Microsoft Azure and Fabric technologies. Leads technical delivery, governance, quality, risks, and timelines while mentoring data engineering teams. Collaborates with architects, clients, and stakeholders to translate requirements into technical solutions and drive Agile, secure, production-ready implementations.

Maintain and enhance Cloudera, Elastic Stack, and Dataiku platforms; develop data pipelines, streaming solutions, dashboards, integrations, and ML deployments. Automate infrastructure and CI/CD processes, troubleshoot platform issues, provide user support and training, and create reusable technical documentation. The role requires strong experience with data platforms, Hadoop ecosystem technologies, Python or Scala, REST APIs, ETL, infrastructure automation, and enterprise analytics environments.

Design, build, and operate production-grade generative AI applications (copilots, RAG pipelines, agents). Partner with business teams from discovery through rollout, implement eval-driven testing, observability, cost controls, security/privacy/compliance, and codify reusable patterns and runbooks. Own integration with enterprise systems and apply responsible-AI governance.

Own search quality metrics, analytics architecture, Python-based data pipelines, dashboards, and measurement tooling. Translate product and technical requirements into actionable metrics, investigate anomalies and regressions, improve measurement approaches, and collaborate with product managers, engineers, and stakeholders. The role requires search relevance or ranking analytics experience, statistical knowledge, strong analytical judgment, independent problem-solving, and effective communication.

Operations Intern supporting Uber’s Mobility business through data analysis, strategic problem-solving, experimentation, and execution. Responsibilities include analyzing datasets, developing operational insights, improving pricing and demand initiatives, supporting customer operations, and advising teams on decisions. The role involves collaboration across operations, finance, policy, communications, product, marketing, legal, and customer support.

Lead equity coverage, write thematic research, build and update financial models, analyze data, monitor earnings and news, distribute market commentary, present to clients and sales teams, and develop relationships with investor relations contacts and industry experts.

Own the reliability, performance, security, resilience, and evolution of SQL Server and Azure SQL platforms. Responsibilities include database operations, performance tuning, backup and disaster recovery, monitoring, automation, incident response, access governance, and modernization toward PostgreSQL, cloud-native services, Databricks, and lakehouse architectures. The role partners with platform, data, and operations teams to maintain critical services and support cloud migration and emerging analytics and AI workloads.

Build and operate a production Azure data and AI platform. Responsibilities include Azure landing zones, network architecture, identity, governance, monitoring, Databricks and Unity Catalog administration, data integrations, MLOps pipelines, model deployment, CI/CD, infrastructure as code, security scanning, self-service tooling, documentation, and on-call support. The role requires hands-on Terraform, Azure, Databricks, containerization, observability, and Python or PowerShell experience.

Owns the reliability, performance, security, and evolution of SQL Server and Azure SQL platforms. Responsibilities include database provisioning, upgrades, performance tuning, backup and disaster recovery, monitoring, incident response, automation, access controls, compliance, and modernization toward PostgreSQL, cloud-native services, Databricks, and lakehouse architectures. The role serves as a database subject matter expert collaborating with platform, data, and operations teams.

Build and operate an Azure-based data and AI platform through hands-on infrastructure engineering. Responsibilities include designing Azure landing zones and networks, managing governance and identity, deploying Databricks and data services, building MLOps pipelines, implementing DevSecOps practices, creating self-service tooling, and supporting platform incidents. The role requires Terraform, Azure, Databricks, CI/CD, containerization, security, observability, and Python or PowerShell expertise.

Design, build, and maintain SOAR playbooks and automated incident-response workflows; integrate security, IT, and cloud systems; deploy cloud security infrastructure via IaC; apply AI/LLM tools to security automation; collaborate with SOC, detection engineering, and IR teams; measure and report automation performance and ROI to leadership.

Designs, builds, and operates secure, scalable GCP data platforms for financial-sector clients. Develops batch and streaming pipelines, architects data lake and warehouse solutions, manages infrastructure through Terraform and CI/CD, and implements governance, security, monitoring, and data quality practices. Collaborates with stakeholders, communicates architecture decisions, mentors engineers, and promotes DataOps and engineering standards.

Own and modernize IG’s Akamai edge delivery estate by migrating configuration to Terraform, building GitLab CI/CD self-service workflows, and automating API-driven operations. Manage routing, DNS, caching, redirects, traffic management, security controls, certificates, observability, and incident diagnosis across global staging and production environments. Partner with application teams and platform stakeholders to deliver secure, resilient, high-performance edge infrastructure under formal change-control processes.

Develops insurance risk analytics and loss models using exposure, policy, claims, pricing, engineering, and hazard data. The role validates catastrophe and non-catastrophe models, applies actuarial and pricing techniques, creates dashboards and decision-ready insights, and collaborates with product, engineering, data, brokers, and client stakeholders. It also documents methodologies, assumptions, governance, and data lineage while helping operationalize scalable analytics solutions in an evolving environment.

Supports Finance Systems Data & Change projects by delivering regulatory and business reporting changes, improving processes, and enhancing efficiency across reporting platforms. The placement includes a 12-month social-impact project, data analysis, problem-solving, teamwork, and structured development within Nationwide’s Finance function.

Three-year development programme combining internal audit, data analytics and professional study. Participants analyze data, review controls and processes, investigate risks, collaborate with stakeholders, and develop insights supporting audit conclusions. Training includes Databricks, Python, SQL and AI-enabled audit solutions, with rotations across Internal Audit and another business area. The programme is based in London with at least two office days per week and includes a professional audit qualification.

Leads the adoption of AI and automation across clinical statistical computing and evidence-generation workflows. Responsibilities include defining adoption roadmaps, translating business needs into technical requirements, redesigning QC and review processes, managing stakeholders, driving training and change, ensuring GxP and validation compliance, and prototyping solutions using R or Python. The role requires practical knowledge of clinical data standards, statistical programming, LLMs, human-in-the-loop design, and regulated technology delivery.

Leads the adoption of AI and automation across clinical research and statistical computing workflows. The role translates business needs into technical solutions, redesigns QC and review processes, manages stakeholders, drives change and capability building, and ensures compliant, inspection-ready implementation under GxP and CSV requirements. Responsibilities include prototyping, code review, AI risk assessment, human-in-the-loop design, training, adoption measurement, and scaling pilots into sustained production practices.

Lead technical design, delivery, governance, and operation of cloud-native data and analytics products on GCP. Provide DevOps and data engineering leadership, implement secure scalable architectures, CI/CD, infrastructure as code, observability, data integrations, and compliance controls. Remain hands-on in solution design, coding, reviews, and complex problem-solving while mentoring engineers, shaping technical strategy, partnering with stakeholders, and driving continuous improvement.