Top Python Jobs in London

The role requires developing ML and quantitative systems using financial time series data, deploying models, and improving signal evaluation for investors.

Lead and grow a TPM function for the Data org, owning delivery of data platform, ingestion, pipelines, enrichment, labeling, and dataset quality. Drive flagship programs, establish scalable governance and KPIs, coordinate cross-functional dependencies, and partner with engineering to improve data quality, throughput, cost, and time-to-insight.

Develop stock selection signals, maintain models, research factors, enhance infrastructure, and assist in portfolio management while communicating findings.

Lead the Risk Platform Operations team to ensure accurate and timely delivery of portfolio risk analytics, mentor junior members, and enhance data quality controls.

As a BI Data Analyst, you will analyze commercial data, assist teams with data queries, track revenue changes, and run tests for product performance.

As a Senior Python Developer, you will innovate solutions for online shoppers, collaborate with teams, and work on scalable products.

Develop computer vision models for object recognition and video understanding while evaluating and improving their performance for real-world applications.

Lead and implement architecture and code for resilient backend systems, embedded hardware, secure networking, and ML inference at the tactical edge. Mentor engineers, run code review and RFC processes, optimise performance and reliability, and drive cross-team technical initiatives from prototype to production.

The HPC Operations Engineer provides operational support for Linux HPC environments, manages problems, collaborates on coding, and oversees maintenance tasks.

As a Data Engineer, you'll optimize the Data Lake and develop pipelines for data-driven decision-making, collaborating with Data Science for efficiency.

Design, build, and operate Kubernetes infrastructure for high-demand workloads, ensuring security, reliability, and scalability while collaborating with research and trading teams.

Manage and improve trading operations and dataflows: own SOD/EOD reconciliations, pricing, settlements, PnL reporting, intraday monitoring, design alerts and reconciliations, streamline trade execution and position distribution, onboard traders/products, evaluate new markets, and collaborate with developers on databases and trader tools.

Build and maintain reliable ingestion and transformation pipelines on Databricks or Snowflake, design Data Vault 2.0/Kimball models, ensure production reliability, support testing/monitoring/documentation, and help apply LLMs and AI-enabled solutions in regulated financial services environments.

Lead hands-on delivery of production data platforms on Databricks or Snowflake, build reliable ingestion pipelines and data models, mentor junior engineers, integrate LLM/AI where appropriate, ensure production reliability and compliance, and support pre-sales technical sizing and proposals.

Lead a London-based connectivity engineering team building proxy, OAuth, and token-management infrastructure for Anthropic products. Hire, coach, and develop engineers; drive roadmap, cross-org alignment, reliability, incident response, and engineering best practices; collaborate with SF teams and partner product, security, and platform groups.

The Data Scientist designs and deploys data models and automation solutions, collaborating with teams to meet business needs through data-driven insights.

The Technical Lead Manager will manage a team of developers, oversee project management, develop features, ensure code quality, and maintain documentation.

The role involves building and implementing machine learning models, collaborating with cross-functional teams to solve complex problems in data science.

Lead technical pre-sales for Algolia by partnering with Sales to demo the product, run POCs, build prototypes/integrations using Algolia APIs, qualify customer goals, respond to RFPs, and transition accounts to post-sales. Advocate customer needs to Product and Engineering, produce technical content, and enable internal teams while supporting customer adoption and success.

Own the end-to-end technical relationship for a single global financial-services account: drive discovery, design PoCs, architect Snowflake-based data and AI solutions, lead technical evaluations, mentor junior SEs, and coordinate across Product, Services, Support, and Engineering to accelerate adoption and expansion.

Lead technical development and production deployment of advanced ML solutions (including LLMs, RAG, graph and sequence models) to improve customer operations. Provide hands-on engineering, mentor ML engineers, collaborate with product and backend teams, and drive MLOps and lifecycle strategy to route, disambiguate and resolve customer queries at scale.

Lead and deliver technically complex backend and full-stack projects, simplify architecture, mentor engineers, use AI-assisted tooling, and partner with product and design to build reliable, high-impact solutions.

Monitor, triage and respond to cyber security incidents using SIEM tools; develop run books, detection use cases and dashboards; research threat intelligence; remediate events and improve GSOC defensive capabilities operating on a follow-the-sun shift rotation.

Lead and grow a data science team to design, develop, evaluate, and deploy production-grade AI/ML solutions (LLMs, Generative AI, RAG, transformers). Own technical vision, MLOps/LLMOps, model governance, and cross-functional execution with Product and Engineering to deliver scalable business impact in financial markets.

Partner with Sales to lead discovery, design solution architectures, deliver demos/POCs, run workshops, support RFx, and build trusted technical relationships to drive adoption of OneSpan digital identity and anti-fraud solutions.