Top Python Jobs in London

Lead EMEA Markets Treasury resource management across balance sheet, capital, liquidity, and leverage. Partner with trading desks and Corporate Treasury to optimize financial resource usage, analyze desk profitability, assess regulatory impacts, and provide guidance on clearing, collateral, and liquidity products. Develop treasury analytics and data tools with technology and quantitative teams while advising senior stakeholders on financial resource strategy.

Develop and productionize generative AI products and augmented reality Lenses focused on image and video generation, editing, and LLMs. Build diffusion and flow-matching pipelines, evaluate open-source models and third-party APIs, and rapidly ship consumer-facing solutions. Collaborate with Product, Engineering, Lens Content, and Data Science teams to prototype, integrate, monitor, and improve models using A/B testing and user feedback. Lead complex technical initiatives while staying current with machine learning research.

Owns tuning and governance of transaction-monitoring, sanctions-screening, fraud-detection, and risk-scoring systems. Analyzes alerts, false positives, case outcomes, and transaction data to improve detection logic and thresholds. Translates financial-crime typologies into detection scenarios, documents decisions with audit-ready evidence, produces monitoring reports, and partners with Compliance, Investigations, Product, Engineering, and MLRO teams across multiple jurisdictions.

Lead data science initiatives from conception through deployment, developing statistical models and machine learning algorithms for trading intelligence. Analyze large datasets, integrate solutions into production systems, support automated trading strategies, communicate insights to stakeholders, and mentor junior data scientists. Collaborate cross-functionally to improve trading processes, product offerings, and business operations.

Develop statistical and machine learning models for DraftKings Sportsbook, engineer sports data assets, implement pricing engines in Python, and integrate models into production applications. Build automated tests, validate data flows, support deployment and monitoring across the machine learning lifecycle, and collaborate with Trading, Product, Engineering, and QA. Coach junior data scientists and contribute to sports modeling solutions.

Summer internship on the Global Data Science team to develop and optimize time-series forecasting models (ARIMA/SARIMA, XGBoost, LSTM). Support end-to-end data ingestion, back-test datasets, collaborate with traders and analysts, and conduct ad hoc research on energy fundamentals and analytics trends.

Supports FP&A transformation by building finance reporting frameworks, extracting and transforming ERP data, maintaining Alteryx automation workflows, and developing planning, budgeting, forecasting, and cash flow models. Partners with finance, IT, business intelligence, and wider business teams to improve data quality and create standardized commercial tools that support data-driven decisions.

Develop and support predictive models for insurance risk, pricing, and underwriting decisions. Integrate models into production systems, monitor and maintain them, and build reusable modelling tools, pipelines, and processes. Prepare and evaluate data features for predictive value, stability, interpretability, and customer impact. The role requires Python, SQL, machine-learning, version-control, and commercial data-science experience, with Azure ML, Snowflake, and insurance experience desirable.

Build production-quality quantum software by translating algorithms and research prototypes into tested, maintainable components. Integrate quantum workloads with APIs, containerized services, Kubeflow workflows, data infrastructure, simulators, and QPUs. Diagnose issues across quantum and classical execution layers, design hybrid workflows, and apply CI/CD, testing, documentation, version control, and code review while collaborating across research, platform, infrastructure, and applications teams.

Provides technical support and guidance to game developers and publishers using PlayStation network services, APIs, tools, and technologies. Investigates and escalates technical issues, collaborates with internal and regional teams, gathers partner feedback, improves developer-facing tools and documentation, automates workflows, and delivers technical training and presentations.

Develop and maintain Java and Kotlin software for a collateral management platform supporting 24/7 trading. Contribute to distributed, real-time, event-driven systems; trading venue integrations; reference data frameworks; and accounting pipelines for traditional and digital assets. Debug production code, build new functionality, participate in code reviews, and collaborate with experienced engineers while learning financial market infrastructure.

Build and productionize forecasting and machine learning models using weather, climate, and satellite data to generate agricultural, environmental, and risk signals. Own end-to-end pipelines from data ingestion and modeling through evaluation, orchestration, and customer delivery. Improve experiment design, uncertainty analysis, reproducibility, and monitoring while collaborating with science, engineering, product, and market intelligence teams.

Develop and maintain compliance technology systems focused on market surveillance, regulatory reporting, data pipelines, automated controls, and integrations. Translate business and compliance requirements into production solutions, troubleshoot technical issues, implement surveillance scenarios, manage testing and releases, and improve automation, performance, and reliability. Collaborate with compliance, technology, developers, stakeholders, and external vendors while supporting systems across the trading and order execution lifecycle.

As a Senior DevOps Engineer, you will develop, manage, configure, and automate systems within AWS and Azure, collaborating in agile teams and aiming to support high-load scalable systems.

Develop and support scalable valuation, risk, and PnL systems for energy trading. Responsibilities include gathering requirements, designing architectures and data models, writing tested .NET code, supporting Python and desktop quantitative tools, integrating models, improving performance, documenting solutions, and assisting production operations. The role collaborates with quantitative developers, analysts, traders, and business users in a fast-paced front-office environment.

Develop and support predictive machine-learning models for insurance risk assessment, pricing, and underwriting. Integrate models into production, monitor performance, maintain reproducible modelling pipelines, and evaluate new data features. The role requires Python, SQL, version control, and experience delivering commercially valuable data science solutions. Experience with Azure Machine Learning, Snowflake, and general insurance is desirable.

Leads predictive modeling projects from conception through production and monitoring, develops reusable data science pipelines, evaluates features, and supports pricing decisions. The role manages and mentors data scientists, reviews technical work, establishes modeling and software development standards, and collaborates with senior leaders to shape data science strategy. Strong expertise in machine learning, Python, SQL, Git, and commercial data science delivery is required, with insurance and cloud ML experience preferred.

Lead predictive modeling projects from conception through production and monitoring, develop reusable data science pipelines, evaluate features, and guide a team of data scientists. The role partners with senior leaders to shape data science strategy, improve modeling practices, and deliver commercially valuable solutions for insurance pricing.

Develop and maintain full-stack Trading and Pricing applications. Translate business needs into technical requirements, design and implement React, Django, and Python solutions, integrate APIs and databases, test and deploy software, and support production releases. Collaborate with stakeholders and engineering teams on architecture, Agile delivery, CI/CD, cloud infrastructure, Terraform, and serverless services.

Build Tailscale’s internal AI-enablement tools, workflows, guardrails, and practices. Partner with engineering leadership and technical teams to define the AI roadmap, evaluate coding agents and models, improve developer velocity and quality, and drive safe adoption. The role includes internal tooling, developer advocacy, documentation, impact measurement, and collaboration across engineering and non-engineering teams.

Senior full-stack engineer responsible for designing and delivering Prime brokerage features across backend services, frontend applications, and operations tooling. The role covers institutional onboarding, settlement, lending, credit, reporting, and tokenized securities. Responsibilities include designing for failure, owning production systems handling real financial value, mentoring engineers, conducting design-focused code reviews, and supporting an on-call rotation. The position requires in-person attendance in London at least three days per week.

Build and maintain batch and streaming data pipelines for the QiCore warehouse. Migrate acquired-business data into a unified platform and single customer view, model data for analytics, and ensure pipeline reliability, governance, testing, and quality. Monitor and resolve data issues at their root, support future acquisitions and migrations, and collaborate through code reviews in a fast-paced team environment.

Own the architecture and design standards for the QiCore data warehouse, including governance, data quality, privacy, access controls, customer identity, and retention. Lead migration of acquired businesses into a unified data model, establish repeatable integration patterns, support AI initiatives, prototype and debug pipelines, and guide data engineers. Partner with technical, analytics, commercial, security, privacy, and governance stakeholders to translate business needs into scalable data solutions.

Owns FinOps and AWS rightsizing across the R&D organization. The role provides cloud cost visibility, allocation, forecasting, variance analysis, optimization backlogs, cost-impact assessments, and recurring financial reporting. It partners with engineering, data, operations, finance, and leadership teams to improve resource efficiency, accountability, and cloud investment decisions while balancing cost, reliability, security, and performance.

Design and evaluate AI systems for real-world energy applications. Responsibilities include building evaluation frameworks and datasets, experimenting with model selection, fine-tuning and reinforcement learning, developing reliable agent harnesses, analyzing failures, setting risk thresholds, and translating research into production systems. The role requires strong Python software engineering, rigorous experimentation, and judgment around cost, latency, reliability, and human oversight.