Top Python Jobs in London

Perform product and behavioural analysis (funnel, cohort, segmentation), define KPIs, run A/B tests, build dashboards, partner with Product and Analytics Engineering, and apply AI/ML techniques to drive product decisions and experimentation.

Lead high‑impact AI and decisioning projects from discovery through production. Define technical roadmaps, design and validate models (including causal/experimentation), partner with engineering to productionise services with monitoring and governance, mentor data scientists, and promote responsible AI practices and platform adoption.

Lead and grow a team of ML engineers to build and operate production ML and LLM systems—real-time inference APIs, batch data products, and agentic AI—ensuring deployment, monitoring, CI/CD, reproducibility, and measurable business impact.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Lead design and deployment of ML models and pipelines for the AI platform. Translate business problems into measurable ML solutions, design experiments and A/B tests, and own model performance, monitoring, degradation detection and retraining. Champion responsible, explainable AI in a regulated financial context, choose between classical ML, LLMs and agentic approaches, collaborate with ML engineering, product and commercial teams, and mentor junior data scientists.

Design, build, operate and optimise scalable data pipelines and platform components. Deliver production-ready solutions, support production incidents, improve data quality, and collaborate across engineering and analytics teams.

Partner with cross-functional product teams to analyse large behavioural datasets, define product metrics, design and evaluate A/B tests, build measurement frameworks, and deliver insights via visualisations to influence product roadmaps and drive commercial outcomes.

Build, deploy, and maintain production ML and AI systems: model packaging, lifecycle automation, monitoring, batch and real-time workloads, and platform improvements while collaborating with data scientists, engineers, and product teams.

Lead a data science team building always-on predictive models and pipelines for an AI platform. Own model lifecycle: production deployment, monitoring, drift detection, retraining, and measurable business outcomes. Provide technical leadership, code reviews, architecture decisions, and stakeholder collaboration. Hire and mentor data scientists, champion responsible and auditable AI, and adopt ML platform tooling (MLflow, model registries, Databricks) and LLM/RAG approaches where appropriate.

Design, build, and deploy machine learning solutions to improve customer journeys, pricing, and personalization. Perform feature engineering, statistical analysis, A/B test design and evaluation, collaborate with cross-functional teams, and support model deployment, monitoring, and responsible AI practices. Mentor juniors and contribute to team standards and knowledge sharing.

Design, build, and maintain scalable, fault-tolerant serving platforms for LLMs. Integrate models for production and research, implement monitoring and observability, perform architecture reviews, and lead cross-functional end-to-end projects enabling LLM capability discovery and high-performance serving.

As a Senior Data Scientist, you will build and maintain production models for pricing decisions, define pricing dynamics, and measure strategy impact within a collaborative team.

Lead the Trading Anti‑Fraud team to detect, investigate, and prevent trading fraud across products and regions. Enhance monitoring rules, manage alerts and cases, collaborate with Trading/Risk/Product/Compliance, and provide strategic reporting and recommendations to senior leadership to reduce fraud losses and improve controls.

Build, own, and maintain internal benchmark suites for single- and multi-turn content and agentic guardrails; create benchmarks distinguishing model capabilities; extend evals to new verticals and product features; collaborate on research quantifying realistic LLM and agent failure modes; support product integration of core model functionality.

As a Software Engineer at Wexler, you'll architect and build full-stack applications, develop features, collaborate with AI engineers, and contribute to technical decisions while working in a fast-paced startup environment.

Act as a technical customer-facing lead to drive adoption of Veracode solutions: onboard customers, integrate products across CI/CD toolchains, automate via APIs, resolve complex app/cloud security issues, present to stakeholders, and feed customer insights to product teams.

Hybrid Business Systems Analyst role supporting Treasury Analytics and Production Support. Responsibilities include eliciting business requirements, implementing Azure/Spark solutions, writing Spark SQL, supporting FIS Quantum and TBSM, L1/L2 incident management, change deployments, troubleshooting, process improvement, and documentation within ITIL/Agile frameworks.

Lead the evolution of Tenable's multilingual delivery platform toward AI/LLM-led localization. Design event-driven, API-first translation pipelines, integrate LLMs into production workflows, extend AI-led delivery across content types, define standards and governance, build AI-powered reporting, and deliver key initiatives while coordinating distributed and contracted engineering partners.

Oversee fiat and crypto treasury operations including settlement, lifecycle event management, reconciliations and controls. Collaborate with product, trading, risk, treasury and finance to optimize funding and mitigate risk. Develop automation for risk monitoring, P&L attribution and reconciliation processes; investigate cash breaks and ensure scalable operational workflows for market expansion and regulatory compliance.

Join the SRE team to build and operate an internal developer platform ensuring high uptime and scalable production releases. You’ll automate, monitor, and support distributed containerised systems, contribute to CI/CD, troubleshoot incidents, and collaborate across teams while learning and applying tools like Kubernetes, AWS, Terraform, Kafka, Go and Python.

Design, deploy, and operate lossless RDMA-capable leaf-spine fabrics for multi-tenant GPU clusters. Automate provisioning, build telemetry/observability, troubleshoot end-to-end performance, manage out-of-band and office networks, support tenant onboarding, and document designs while upskilling colleagues.

Join the Financial Engineering team to develop innovative reporting solutions, enhance existing systems, support clients, and contribute to new initiatives.

Design, develop, and support Azure-based cloud integrations and platform services. Build API/queue/file-based integrations, improve CI/CD and deployment reliability, troubleshoot production issues, modernize legacy systems to cloud-first patterns, and document and transfer service ownership to reduce operational risk.

Lead design and delivery of production-grade AI agent services: build LangChain-driven LLM workflows, design distributed microservices, implement testing/evals and monitoring, deploy and maintain models, guide architecture decisions, write production-quality code, troubleshoot live systems, produce documentation, and mentor junior engineers.

Join a Data & AI team building production LLM-powered agents for underwriting. Own end-to-end data science for production systems: prototype with stakeholders, design evaluation frameworks (offline and online), build analytical pipelines, measure agent quality, and work with engineers to productionise monitoring, CI/CD, and reliability.