Top Python Jobs in London

Lead bioinformatics strategy and execution across Discovery client engagements. Design and implement computational multi-omics workflows, translate biological questions into analytical frameworks, deliver client-ready insights, mentor junior staff, support business development, and help grow internal bioinformatics capabilities and thought leadership.

Own the customer-facing support product experience, including FAQ, self-service, Help Center, Wise Assistant, and conversational automation. Develop product strategy and multi-quarter roadmaps, collaborate with design, research, analytics, content, and engineering teams, and use experimentation and data to improve customer support, reduce friction, and increase automation rates.

Conduct applied AI research across the LLM lifecycle, including training, inference, agentic systems, evaluation, explainability, and reliability. Build and deploy practical AI systems from prototype to production, develop methods for detecting hallucinations and verifying workflows, and collaborate with researchers and business teams. The role includes documenting findings, presenting to technical and non-technical audiences, contributing to intellectual property, and publishing or presenting research at industry conferences.

Designs, develops, and optimizes distributed computing solutions across HPC, Kubernetes, and cloud environments. Builds software in C++, Python, and Go; deploys applications using Kubernetes and HPC/grid technologies; and applies generative AI and observability tooling to improve monitoring and performance. Collaborates with cross-functional teams to deliver secure, scalable compute platforms.

Lead Finance AI strategy, portfolio prioritization, and initial Accounting-focused deployments. Establish reusable delivery patterns, governance, controls, evaluation criteria, monitoring, and production standards for AI workflows. Partner cross-functionally with Finance, Risk, Data, Engineering, Product, and Platform teams, while building the permanent Finance AI Delivery function through hiring and coaching. Prototype solutions using Python, MCP servers, tools, and no-code agents, and oversee scaling, performance, reliability, and operational readiness.

As a Quantitative Researcher intern at Citadel, you'll develop models, implement trading strategies, and conduct statistical research in finance.

Build and operate AI-powered developer tools, agentic workflows, reusable skills, integrations, orchestration capabilities, backend services, and APIs. Integrate AI systems with source control, CI/CD, issue tracking, documentation, and developer environments. Apply testing, evaluation, monitoring, security, and feedback practices to non-deterministic AI systems while collaborating with developers and contributing to platform architecture and engineering processes.

Owns solution architecture for complex secure networking opportunities in a senior presales capacity. Leads customer discovery, high-level design, proposals, pricing support, vendor strategy, and services integration across enterprise, data center, WAN, campus, cloud, and security environments. Presents to stakeholders, aligns technical solutions with business and commercial outcomes, supports pipeline growth, and transitions designs to delivery teams. Requires extensive networking architecture experience, strong customer engagement skills, multi-vendor expertise, and familiarity with automation, cloud networking, observability, and Zero Trust.

Build and lead scalable software solutions that automate compliance and finance regulatory reporting. Process large datasets from Snowflake, generate accurate reports, build reconciliations, and translate stakeholder requirements into technical designs. Provide technical guidance, prioritize work, develop and review code, troubleshoot issues, and improve engineering standards. Collaborate closely with Compliance, Finance, Treasury, product managers, and other business stakeholders.

Leads the end-to-end development and deployment of data products for Asset Management. Translates business needs into product requirements, coordinates data sourcing and curation, ensures governance and quality standards, supports adoption and value realization, and improves the data product operating model. The role also evaluates existing products for rationalization and provides hands-on expertise across business, data, and technology teams.

Lead the architecture and production deployment of deep learning systems for real-time financial crime detection. Design sequence, graph, and attention-based models; establish reusable ML pipelines from experimentation through deployment; evaluate foundation models and embeddings; make architecture decisions under latency and throughput constraints; partner with data science on evaluation and causal measurement; and mentor engineers and scientists.

Develop and deploy production AI/ML applications for digital banking, using Python, SQL, cloud platforms, LLM patterns, classical machine learning, and data analysis. Responsibilities span requirements gathering, solution design, prototyping, evaluation, deployment support, monitoring, governance, and iteration. The role collaborates with product, operations, marketing, and technical teams to deliver customer and operational improvements while supporting risk, privacy, and model controls.

Develop and operate Python-based quantitative models and production systems for FX pricing, risk management, forecasting, hedging, and trading. Responsibilities include quantitative research or engineering, backtesting, model validation, monitoring, deployment, incident response, shared library development, CI/CD, and real-time system reliability. The role collaborates with quants, traders, analysts, product managers, engineers, and risk teams.

Supports production technology and LLM-powered systems by monitoring application flows, troubleshooting incidents, developing reliable AI microservices, and implementing LLMOps practices. Responsibilities include L3 incident management, retrieval and vector systems, prompt and model lifecycle controls, guardrails, data quality, observability, CI/CD integration, resiliency, security, and regulatory documentation. The role also develops architecture artifacts, evaluation frameworks, deterministic fallbacks, and secure production code for enterprise AI applications.

Lead the Execution DevOps team for a global futures and options trading platform. Define DevOps strategy, build CI frameworks, automate testing and delivery, improve observability and change management, and own resiliency, failover, and recovery outcomes. Partner across application development, integration, business analysis, and production management to deliver reliable 24/7 execution technology. Drive modernization, AI-enabled automation, and operational excellence across exchange connectivity, algorithmic trading, and market data.

Design, provision, and operate a scalable, secure cloud data platform on AWS and Databricks using Terraform, automation (Python/Java), CI/CD, observability, and security best practices to support production analytics workloads.

Manage index commodity and synthetic equity derivative portfolios, oversee exposure, cash and liquidity, apply quantitative models for construction and risk budgeting, launch and modify portfolios, perform risk and performance attribution, build investment platform, and support client communication and sales.

Analyze large, multi-source datasets using SQL and Python, R, or similar tools to generate insights for hotel partnerships and commercial decisions. Build reusable datasets, reporting solutions, visualizations, dashboards, and basic data workflows. Apply descriptive statistics, regression, experimentation, and causal analysis while clearly communicating assumptions and recommendations to technical and non-technical stakeholders. Collaborate with analytics, product, engineering, data, and commercial teams.

Eight-week quantitative investing internship at BlackRock for specialized master's students. Interns contribute to investment or client projects, support quantitative research and portfolio management, analyze large datasets, translate statistical models into Python, and learn about capital markets, economic theory, portfolio trading, and investment strategies. Opportunities span quantitative research, portfolio management, product strategy, and investment technology across systematic equity and fixed-income teams.

Lead growth & adoption analytics by building and maintaining agent-ready curated datasets, partnering with engineers and product teams, reviewing merge requests, defining pipelines and metrics, performing advanced SQL analyses on petabyte-scale data, and communicating actionable insights to stakeholders.

Build scalable backend infrastructure and distributed systems for Palantir’s data platforms. Responsibilities include developing search and indexing systems, data processing libraries, container orchestration, APIs, observability, storage, authentication, asset serving, and automated deployment of technologies such as Cassandra, Elasticsearch, Kafka, and Kubernetes. Collaborate with technical and non-technical stakeholders while delivering reliable, secure, high-performance software.

Supports technical sales and customer engagements for Hivemind autonomy solutions across the EURAF region. Translates defense customer missions, CONOPs, and integration constraints into feasible solution concepts, demonstrations, proposals, and technical materials. Partners with business development, product, engineering, and program teams while explaining capabilities, limitations, and roadmap considerations to diverse stakeholders. Builds customer relationships and supports international defense modernization and capture activities.

Designs, calibrates, operates, and improves FX execution algorithms across a segregated electronic trading desk. Responsibilities include algorithm parameter tuning, transaction cost analysis, production troubleshooting, venue and liquidity-provider onboarding, client integration, UAT, market-structure research, vendor due diligence, and compliance with best-execution, information-barrier, FCA/PRA, and FX Global Code requirements. The role partners with quantitative, product, technology, sales, and trading teams in a global 24-hour environment.

Lead the design, development, deployment, and continuous improvement of AI/ML solutions for enterprise customers. Build integrations using Python or JavaScript, APIs, microservices, and enterprise systems; analyze data; automate workflows; address AI risk, privacy, and compliance; and align technical solutions with business goals. Mentor engineers and communicate effectively with technical and non-technical stakeholders in complex, ambiguous environments.

Provide technical customer support through email, live chat, and forums; triage bugs and requests, meet response SLAs, troubleshoot machine learning product issues, and collaborate with engineering and product teams. Maintain product knowledge and documentation, identify recurring issues, improve support processes, and participate in a 24/7 weekend on-call rotation. The role is based in central London three to five days per week.