Top Python Jobs in London

Monitor autonomous satellite laser ranging stations, triage faults, coordinate fixes with field engineers and host sites, and maintain reliable network operations. Build dashboards, alerting, and runbooks; track network performance; report results to internal teams and customers; support tracking schedule planning; and help develop on-call coverage as the global station network expands.

Build and deploy practical machine learning, deep learning, and generative AI solutions. Design and evaluate experiments, work with large language models and AI agents, write production-quality code, optimize model performance, and collaborate with product and engineering teams to deliver scalable AI systems.

Build and deploy predictive models across credit acquisition, portfolio management, collections, fraud, and rewards. Own the full modeling lifecycle, including feature engineering, validation, implementation, monitoring, and drift analysis. Work directly with risk and commercial teams to improve decisions and measurable outcomes. Help establish Yonder’s data science standards, tooling, experimentation practices, and development approach while clearly communicating model insights to technical and non-technical stakeholders.

Supports structured finance transactions from initial analysis through closing and ongoing management. Builds cash flow, returns, sizing, scenario, and stress models; monitors CLO and asset-backed markets; prepares investor materials and diligence responses; coordinates with counsel, rating agencies, and deal teams; researches regulatory regimes; and drafts product papers and investment committee memoranda.

Build practical machine learning, statistical, and data science systems for Prolific’s participant marketplace. The role focuses on modeling participant supply, demand, and quality; improving matching to studies; validating useful signals; designing experiments and metrics; and deploying production-ready solutions. The scientist will independently shape ambiguous problems, select appropriate approaches ranging from classical statistics to LLMs, and collaborate with product, engineering, and Supply Data Science specialists.

Own production forecasting and strategy models end to end, including data infrastructure, statistical and machine learning model selection, experimentation, evaluation, and decision-ready reporting. The role requires Python and SQL expertise, strong experiment design, and the ability to communicate complex analyses to non-technical stakeholders while collaborating with the analytics team and responsibly using AI.

Lead Markets Treasury strategies optimizing balance sheet, liquidity, leverage, collateral, counterparty credit risk, market risk, and regulatory capital. Analyze financial resource consumption, portfolios, trade structures, and liquidity methodologies to identify optimization opportunities. Partner with trading desks, technology and quantitative research teams, vendors, and clearing houses. Develop data-driven optimizers, benefit trackers, and analytics while guiding methodology improvements and strategic roadmaps.

Build and scale shared AI platform capabilities on Azure, with emphasis on agentic AI runtimes, orchestration, tool integration, LLMOps, evaluation, observability, security, and reliability. Develop reusable templates, standards, and reference implementations for enterprise AI adoption. Partner with product, infrastructure, security, and engineering teams to establish governed model access, lifecycle management, CI/CD, and production-ready AI operating practices.

Designs, builds, and deploys AI-powered production intelligence software for global SaaS infrastructure. Responsibilities include developing resilient microservices, AI agents, MCP integrations, observability pipelines, anomaly detection, incident response, self-healing automation, and SRE practices such as SLOs, error budgets, and post-incident reviews. The role also mentors engineers, conducts code reviews, establishes engineering standards, and coordinates cross-team delivery.

Design and validate automated workflows for assays, support lab automation systems, troubleshoot equipment, and lead project collaborations in R&D.

Develop and maintain low-latency football data acquisition and analysis systems using social media and other unstructured sources. Build relational database schemas and RESTful API integrations for modeling and trading systems, optimize system performance, and integrate LLM-based pipelines that convert unstructured data into structured outputs. Collaborate with football analysts to ensure data quality and utility.

Develops end-to-end data solutions, including exploratory analysis, experimentation, predictive and prescriptive modeling, machine learning, and generative AI applications. Partners with business, product, and technical teams to solve complex insurance challenges, communicate insights, and support data-driven decisions. The role also involves validating AI-assisted work, applying statistical methods, and following engineering practices such as version control, testing, code review, and reproducible development.

Lead the research, design, productionization, and scaling of machine learning models for pricing and shipping optimization. Own high-impact projects, conduct rigorous experiments, apply causal inference and optimization techniques, collaborate with engineers and cross-functional stakeholders, communicate findings, and mentor junior team members.

As an FX Technical Director, create CG effects, simulations, and animations. Collaborate with teams to deliver high-quality visual effects for films.

Provides technical leadership for enterprise data engineering domains, designing and delivering scalable data platforms, pipelines, data products, and lakehouse solutions. The role defines engineering standards, promotes CI/CD, testing, observability, security, governance, and data quality, while translating business requirements into technical designs. It also mentors engineers, reviews architecture and code, supports operational reliability, and collaborates with architects, product owners, analysts, data scientists, platform teams, and business stakeholders.

Develop quantitative foundations for a risk and PnL management platform supporting trading across asset classes. Build risk models, scenarios, performance attribution, pricing integrations, and scalable analytics infrastructure. Collaborate with traders, quantitative researchers, pricing quants, and technology teams to translate requirements into production systems for real-time risk and performance management.

Lead Linux platform engineering for high-performance trading and research infrastructure. Responsibilities include workload scheduling, large-scale systems management, high-performance filesystem operations, software lifecycle management, kernel and network performance tuning, troubleshooting complex system issues, infrastructure automation, CI/CD, observability, and container orchestration. The role partners closely with research and infrastructure teams and provides technical leadership, strategic insights, and proof-of-concept evaluations.

Leads the design, development, deployment, and evaluation of machine learning, forecasting, recommendation, and analytical solutions. Partners with Product, Commercial, Engineering, and Data teams to solve business problems, define metrics, build scalable pipelines, and deliver measurable impact. Provides technical leadership and mentorship, promotes model development and governance best practices, and contributes to the data science roadmap.

Own the SFMC-to-Braze migration and develop a trusted marketing data source of truth across 40+ tools. Responsibilities include data architecture, schema and identity resolution, SQL and Python validation, pipeline debugging, data quality monitoring, AI-assisted workflow automation, vendor management, and cross-functional coordination with data and technology teams.

Own global organic growth across traditional search, AI-driven discovery, and social search. Lead technical SEO strategy for a high-volume marketplace, improve indexability and content discoverability, connect search performance to registrations and auction activity, and guide product, engineering, editorial, and external agency partners. The role requires hands-on technical expertise across complex architectures, international markets, generative search, and enthusiast-focused content.

Develops interactive, static, and animated data visualizations that transform complex commodity, maritime, and geopolitical datasets into actionable narratives. The role involves querying and validating data with SQL and Python, applying GIS and spatial analysis, building web-based interactive experiences with JavaScript or TypeScript, collaborating cross-functionally, and mentoring peers on visualization and storytelling standards.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Develops computational models and parametric workflows for architecture, urban design, planning, landscape architecture, and engineering projects. Integrates 3D modeling, GIS, BIM, software development environments, and diverse project data. Collaborates with clients and multidisciplinary teams, provides technical education, communicates design concepts, and creates project presentations and reports. Requires strong Rhino and Grasshopper expertise, Python programming, relevant professional experience, and a portfolio demonstrating computational design work.

Leads the strategy, people, technical direction, governance, and delivery of data science, analytics, and AI capabilities supporting pharmaceutical R&D portfolio investment and resource-management decisions. Builds and develops multidisciplinary teams, delivers production-grade analytics applications, partners with senior stakeholders, establishes standards and data-quality practices, and promotes responsible AI in a regulated environment.

Build and maintain scalable, secure data infrastructure and ETL pipelines supporting data-driven sports pricing and risk systems. Responsibilities include data architecture, relational database design, SQL and Python development, Kafka-based pipelines, AWS services, storage and recovery, data security, systems integration, troubleshooting, optimization, documentation, and collaboration with clients and cross-functional stakeholders.