Top Python Jobs in London

As a Lead Value Engineer, you'll drive data-driven business transformations for clients, ensuring successful adoption of Celonis technologies, managing value workshops, and translating strategic objectives into actionable insights.

Own Zip’s end-to-end demo engineering function by building and operating demo environments, datasets, scripts, reusable tooling, narratives, and certification standards. Automate demo asset generation with AI, validate workflows using Playwright, prototype with AI-native tools, and design agentic loops that prepare, test, and repair environments. Partner with Solution Engineering, Product, Product Marketing, and Enterprise Architecture to keep demos aligned with product releases and enable consistent self-service delivery for sales teams.

Implement and configure Zip integrations for enterprise customers across ERP, procurement, ticketing, CLM, and TPRM systems. Design integration architectures, validate API requirements, configure solutions using iPaaS tools, test and troubleshoot integrations, document technical solutions, provide project estimates and status updates, and transfer knowledge to customers. Collaborate with sales, solutions, product, and engineering teams to improve integration processes and customer outcomes.

Develop, enhance, and maintain insurance pricing models and frameworks using Hyperexponential, Excel, and analytical technologies. Partner with underwriters, actuaries, portfolio management, IT, and change teams to translate pricing needs into practical solutions. Analyze data, support calibration and validation, document models, improve underwriting outcomes, and contribute to pricing transformation initiatives. The role also involves adopting AI-enabled tools, sharing knowledge, and supporting junior colleagues.

Diagnose and resolve API and SDK issues in customer codebases, reproduce bugs, collaborate with Engineering on fixes, and communicate technical solutions through Slack and email. Create knowledge-base documentation, improve developer-support workflows and tooling, relay customer feedback to Product and Engineering, participate in on-call rotations, and mentor peers. The role also involves scripting, debugging Python or TypeScript, supporting LLM-powered applications, and improving developer experience and product reliability.

Diagnose and resolve developer issues involving APIs, SDKs, and customer codebases. Reproduce bugs, collaborate with Engineering on fixes, communicate with customers through Slack and email, create technical documentation, improve support tooling and developer experience, relay feedback to Product, participate in on-call rotations, and mentor peers in escalation handling and support processes.

Manage product research and insights across EMEA, serving as a subject-matter expert on active public and private investment products. Conduct competitor, market, client-demand, and asset-flow research; identify product opportunities and strategic gaps; support product development, prioritization, positioning, and lifecycle management. Translate intelligence into commercial recommendations, investment content, and client engagement strategies while influencing senior stakeholders across Product, Distribution, Investments, and Marketing.

Leads Moneybox’s AI Platforms and AI Deployment function, owning platform strategy, governance, security, model hosting, scaling, costs, vendor management, and business enablement. Builds and manages a forward-deployed AI engineering team, establishes safe AI workflows and graduation standards, and partners with Tech Ops and Data Science on customer-facing AI. The role requires hands-on technical leadership, strategic planning, team building, and commercial accountability.

Own end-to-end machine learning systems for Depop’s Trust Detection team, addressing phishing, counterfeit goods, fraud, abuse, and prohibited content. Responsibilities include problem framing, data strategy, deep learning and LLM development, multimodal modeling, deployment, experimentation, evaluation, and production iteration. The role partners with Trust, Policy, and Product teams to build scalable, robust systems in ambiguous and adversarial environments and communicate technical trade-offs and impact.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Lead AI innovation across the agency by mapping business processes, designing multi-agent workflows, integrating multimodal models and external APIs, and deploying secure production systems. The role involves building AI tools for campaigns, creative content, analytics, editorial, video, and internal operations; establishing data retention, privacy controls, human guardrails, and staff adoption. This is a hands-on leadership role requiring Python engineering, cloud deployment, experimentation, and close collaboration across business teams.

Supports equity research on publicly traded real estate securities and REITs by building financial models, valuations, projections, and databases. Analyzes earnings, financial statements, market news, economic forces, and conference calls; drafts research reports and sector updates; develops investment recommendations; and communicates with sales teams, company management, investors, and market contacts. Requires strong finance, valuation, analytical, research, writing, organization, and multitasking skills.

Leads and develops a team of Data Scientists and other Data professionals, setting strategic direction and identifying high-impact opportunities. Partners with Product, Engineering, and senior stakeholders to shape product strategy and influence decisions through experimentation, causal inference, statistical modelling, machine learning, and analytics. Ensures analytical work drives measurable customer and commercial outcomes while collaborating with other data disciplines on tooling and infrastructure. Also contributes to hiring, coaching, technical standards, and the broader Data Science practice.

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Leads technical pre-sales architecture for Salesforce Agentforce and Data Cloud, designing enterprise AI, data, and integration solutions. Builds prototypes and demos, facilitates customer workshops, advises on agent interoperability, prompt engineering, data modeling, governance, and lifecycle management. Partners with account executives, solution engineers, product teams, and customers to integrate Salesforce with hyperscalers, data lakes, external agents, and enterprise systems. Creates technical enablement content and supports early post-sale adoption.

Design and operate production data pipelines and standardized data models for a multi-tenant Azure SaaS platform serving container terminals. Build near-real-time and batch ELT pipelines from operational systems, develop Go-based connectors, implement data quality monitoring, and support source-to-curated transformations across PostgreSQL layers. Collaborate directly with terminal operations staff while owning architectural decisions and platform scalability.

Plan, implement, and maintain identity and access management systems across cloud and on-premises environments. Manage authentication, privileged access, identity governance, cloud IAM, DevSecOps integrations, automation, documentation, incident response, audits, and compliance activities. Advise technical teams on security controls, monitor IAM metrics, support remediation tracking, and coordinate regional incident response using enterprise service-management tools.

Lead hands-on software engineering execution from concept through production. Build AI-powered autonomous testing agents using LLMs, embeddings, vector stores, and retrieval pipelines. Own frontend, backend, cloud infrastructure, experimentation, deployment, and technical direction while collaborating with users and founders to shape the product roadmap.

Build and maintain full-stack Creative Studio products across frontend, backend, APIs, infrastructure, and generative AI workflows. Develop new products and proof-of-concepts, improve existing platform functionality, support enterprise integrations, strengthen reliability and scalability, and process large-scale datasets for generative AI models. Collaborate with Engineering, Growth, Sales, research teams, and customers to solve complex product and workflow challenges.

Designs, develops, tests, deploys, and supports software applications and highly available distributed services. Collaborates with analysts, product owners, architects, QA, and project teams on solution design, estimation, documentation, and delivery. Promotes DevSecOps, automated testing, code quality, and CI/CD practices; provides technical guidance and mentoring; investigates production incidents through third-line support and an out-of-hours rota.

Lead carbon accounting, net-zero strategy, decarbonisation, and sustainability consulting projects for clients. Responsibilities include Scope 1–3 footprinting, SBTi and CDP pathways, transition plans, FLAG emissions calculations, verification, project management, client relationship development, new business generation, report writing, data analysis, and process improvement. The role also contributes to team growth, industry events, and commercially practical solutions that deliver measurable climate impact.

Designs and evolves scalable data pipeline, orchestration, and inference infrastructure for autonomous driving data platforms. Responsibilities include developing DAG-based workflow systems, tooling, shared libraries, batch inference capabilities, and high-throughput processing workflows. The role focuses on reliability, performance optimization, resource prioritization, incident response, and collaboration with partner teams to support large-scale data ingestion, processing, evaluation, and machine learning operations.

Translate business and regulatory requirements into reference data models, datasets, data quality rules, workflows, and documentation. Partner with architects and stakeholders across Trading, Compliance, Operations, and Research. Serve as a subject matter expert on equity and ETF reference data, corporate actions, identifiers, lifecycle processing, and downstream impacts. Support data interrogation using SQL and contribute to system delivery, data dictionaries, entity relationship diagrams, requirements specifications, and exception-management metrics.

Design and scale backend services, REST APIs, microservices, data pipelines, database schemas, caching layers, and event-driven systems for AI-powered media workflows. Integrate machine learning models into production, optimize performance and reliability, implement observability, resolve incidents, and collaborate with ML, DevOps, frontend, and product teams.

Identify and evaluate audiovisual generative AI technologies, prototype proof-of-concept implementations, test research developments, and build scalable ComfyUI workflows. The role involves model training and inference, dataset management, pipeline integration, hyperparameter tuning, collaboration with research, product, and creative teams, presenting findings, and training artists. It is remote across EMEA.