Top Data Jobs in London

Design, operate, and optimise highly available database and storage systems to improve reliability, performance, scalability and observability. Lead platform improvements, automate operations, support data pipelines, participate in incident response and root cause analysis, and collaborate with engineering and data teams to evolve platform capabilities.

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.

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.

Lead MEP delivery for a large data centre project in North London as the Owner's Representative. Manage mechanical, electrical and public health systems from construction to commissioning, oversee subcontractors, ensure integration with commissioning and project controls, control budgets and risks, enforce safety and quality, and support final testing and handover.

Lead and grow a team of data scientists focused on merchant acquisition, onboarding, order management, retention and self-serve tools. Define analytical direction, prioritise high-impact problems, drive experimentation, causal inference and applied ML work, partner with Product/Engineering/Commercial stakeholders, and translate analyses into product, operational and commercial decisions.

Lead strategic commercial engagement and hands-on procurement for Technology and Data vendors. Manage renewals, negotiations, sourcing, vendor consolidation, and risk related to vendor concentration. Support category strategy, pricing analysis, benchmarking, and procurement capability building. Partner with Technology, Finance, Legal and business stakeholders to improve vendor visibility, governance, and enterprise leverage while embedding procurement into strategic decisions.

Develop stored procedures or notebooks to build 230 additional data items into DCoE tables/views. Create an extensible, config-driven process to add fields with minimal re-engineering. Produce self-documenting, well-commented code and collaborate with the DCoE team on delivery; DCoE handles business analysis, design sign-offs, data modeling, and testing.

No job responsibilities provided; the job advert content is missing. Title indicates a senior Data & AI leadership role at Grant Thornton, likely responsible for strategy, delivery oversight, and stakeholder engagement.

Design, develop, and maintain core components of a distributed data platform (storage, compute, RPC/networking, coordination). Collaborate with researchers and engineers to translate requirements, troubleshoot production issues, participate in code review, system design, and productionization.

Lead global strategy and delivery for Market Data Services, managing category strategy, vendor relationships, sourcing, negotiations, licensing/risk, budgets, and service delivery. Partner with senior leaders to optimize spend and data usage, build high-performing teams, and implement scalable supplier governance and sourcing processes.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Build, maintain, and troubleshoot scalable AWS data pipelines using Python, PySpark, Spark, and Airflow. Perform root-cause analysis of pipeline and data-quality issues, apply dimensional modeling and slowly changing dimensions, and deliver governed data assets. Provision infrastructure with Terraform, containerize solutions with Docker, and manage GitLab CI/CD deployments. Work with AWS security, encryption, IAM, monitoring, and analytics services in a regulated government environment.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Build and maintain backend services, APIs, and data pipelines for Modo's energy market platform. Own AWS infrastructure with Terraform, implement monitoring, and contribute to LLM-powered features. Collaborate with analysts and data scientists and split time between new features and platform health.

Drive enterprise sales for Semrush’s big data and emerging solutions. Serve as a subject-matter expert, partner with Account Executives on team selling, conduct product demonstrations, translate complex capabilities into customer value, identify expansion opportunities, provide product feedback, support launches and events, and maintain deep product knowledge. The role requires enterprise sales experience, solutions selling expertise, familiarity with DaaS, SaaS, or big data analysis, and strong storytelling skills.

Design and deliver production-grade AI/ML solutions for Kainos Workday products, including LLM-driven features, model deployment, dashboards, and mentoring junior staff. Collaborate with engineering and clients to improve product functionality, scalability, and efficiency.

Lead data strategy and engineering for AD/ADAS: collect, sample, label, and curate large-scale vehicle and simulator data; build evaluation frameworks and scalable pipelines; analyze data to improve perception and planning models; collaborate across teams and mentor colleagues to deploy production-ready ML solutions.

As an Innovative Project Planner, you will oversee project plans, ensuring efficient delivery through effective resource management, stakeholder communication, and the development of scalable solutions in complex construction projects.

The Innovative Planning Manager will enhance project delivery, manage project plans, create scalable solutions, report to stakeholders, and develop teams in complex construction environments.

Lead acquisition and expansion of Hearst UK's contactable audience through partnerships, paid media, affiliates and co-registration. Own roadmap, test and scale channels, ensure GDPR-compliant data capture and integrations, monitor data quality, and collaborate with CRM, analytics, engineering and product to drive conversion and subscription impact.

As a Project Controls Manager, you'll enhance project delivery through effective cost, schedule, and risk control, while leading team development and creating scalable solutions.

The Senior Data Analyst will enhance courier performance management and optimize handovers by modeling trade-offs, running experiments, and improving data analytics.

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.

Analyze customer behaviour, build customer segmentation and predictive models, create and maintain dashboards, measure CRM program impact, support qualitative and quantitative research, and partner with CRM, marketing, digital and regional teams to translate business questions into actionable insights that drive revenue and improve customer engagement.

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.