Top Python Jobs in London

Lead Marketing Analytics by defining the analytics roadmap, prioritizing business questions, and shaping measurement frameworks (experimentation, attribution, MMM). Partner with Marketing, Product, and Strategy to monitor KPIs, translate analysis into decisions, and hire, coach, and develop a team of Data Scientists to improve marketing outcomes.

Build and maintain ETL/data pipelines into the Data Lakehouse, develop Power BI dashboards, support AWS infrastructure (S3, Glue, Lambda, Step Functions, LakeFormation), ensure data quality using dbt, write complex SQL and Python for data processing, use Git for version control, document workflows, and collaborate with analysts and data scientists.

Design, build, and operate scalable, highly available cloud infrastructure for customer-facing APIs and large model training. Implement automation, CI/CD, container orchestration, monitoring, incident response and on-call duties. Collaborate with engineers on reproducible model training, security, documentation, and tooling; contribute to open-source and reliability improvements.

Design, develop, and maintain cloud-native full-stack applications on AWS. Build Python back-end services and REST APIs, create React/TypeScript front ends, use serverless services (Lambda, DynamoDB, SQS), support CI/CD pipelines, participate in Agile ceremonies, perform code reviews, and troubleshoot across the stack. Work closely with DevOps, QA, and platform teams to deliver secure, scalable public-sector solutions.

Build and ship full-stack features for North, develop autonomous agents that access sensitive enterprise data, productionize state-of-the-art models, write minimal, secure low-resource code, and collaborate with researchers to deploy performant RAG/agentic applications while prioritizing security and user experience.

The Credit Data Analyst will analyze and interpret lending data to support credit decision-making and portfolio management. Responsibilities include building reports, identifying risks, collaborating with teams, and ensuring compliance with guidelines.

The Front Office Developer will design, implement, and support applications for the Derivatives Engine Trading Desk, assisting in hedging, risk management, and pricing. Close collaboration with Trading, Quants, and Technology is essential.

Build and automate month-end reporting and financial controls by streamlining manual processes, orchestrating workflows between ERP/Jira/reporting, developing diagnostic and audit tools, supporting month-end close, enforcing data contracts, and strengthening SOX/IFRS-compliant reporting.

Design, build, and maintain scalable data pipelines and ingestion patterns across GCP and AWS with BigQuery. Own Dagster orchestration, enforce data quality and lineage, integrate acquired firms into a canonical data model, and apply LLMs/AI tooling to automate entity resolution and schema mapping. Partner with Analytics, Product, and AI teams to support self-serve analytics, model training pipelines, and cost/performance optimization.

Build and automate month-end reporting, eliminate manual processes, and embed financial controls. Develop SQL queries and Power BI visuals, orchestrate workflows (Python, Power Automate, n8n, Abacum), support month-end close, enforce data contracts, strengthen SOX/IFRS controls, and act as technical contact for auditors.

Build, test, and ship LLM-powered, agentic AI solutions (RAG, tool-augmented agents) into enterprise workflows. Implement retrieval pipelines, observability, guardrails, CI/CD, and instrumentation; collaborate with stakeholders and deliver reusable patterns and handovers.

The Data Analyst will manage complex data projects, provide analytical support, develop reports and models, and mentor junior analysts.

As a Graduate Software Engineer at Ebury, you will design, develop, test, and deploy software solutions, collaborating with cross-functional teams and participating in the full software development lifecycle.

Own product deployment and customer impact delivery for public‑sector clients. Identify high‑value workflows, build data models and pipelines, write code to integrate data, and cultivate executive relationships to deliver mission‑critical operational outcomes with speed and quality.

Design, build, and maintain scalable data pipelines (Snowflake, Dagster, SQL) to process financial, climate, and ESG data. Integrate climate risk models into production, automate deployments via GitHub/CI-CD, and uphold engineering best practices through testing, documentation, and code reviews while collaborating with data scientists and stakeholders in an agile team.

Own and evolve scalable data pipelines, transformations, and curated datasets for enterprise reporting and analytics. Build data models in warehouse/lakehouse environments, implement data quality/monitoring, and apply CI/CD and version control. Collaborate with analytics, BI, platform, and architecture teams to ensure reliable, governed, and reusable data services.

The Catastrophe Analyst will lead innovative research on catastrophe risk, support model evaluation, and collaborate with various teams to enhance AIG's risk assessment and management practices, especially related to climate change and catastrophe modelling.

The Catastrophe Research Manager leads a team to develop AIG's risk assessments for natural catastrophes and oversees model validation, climate-related analysis, and collaboration across departments.

The Senior Commercial Finance Analyst will analyze commercial performance, maintain financial models, leverage AI for reporting, and collaborate with teams for strategic financial decision-making.

Forward Deployed Engineers at Thought Machine deliver technical solutions by embedding with clients, managing project lifecycles, coding production systems, and facilitating client success with technological implementations.

As a Senior FP&A Commercial Analyst, you will analyze commercial performance, manage financial models, and integrate AI for reporting efficiency while collaborating with marketing and operations teams.

The internship involves applying statistical and mathematical techniques to develop quantitative trading strategies, analyzing financial datasets, and collaborating with researchers and portfolio managers.

The role involves researching, developing and operating an options platform, writing scalable code, designing components, and collaborating with various stakeholders.

The Applied Scientist will develop AI systems for autonomous driving, focusing on machine learning, simulation, robotics, and real-world deployment, while advancing embodied AI technologies.

Collaborate with traders and researchers to design and build robust data systems for trading analysis in a fast-paced environment.