Top Data Jobs in Edinburgh

Lead and maintain clients' asset and reference data, validate investment data, manage data providers, mentor analysts, collaborate on reference data strategy, and ensure compliance with procedures and risk controls.

Leads flagship AI Labs initiatives from scoping and research through production deployment. Designs and evaluates AI agents and agentic workflows, guides production-grade architecture, establishes quality and safety measurement, and partners with business and engineering stakeholders. Manages and mentors 3–6 AI scientists while communicating technical outcomes to senior leaders and broader audiences.

Leads research and production development of generative AI and large language model systems for cybersecurity. Responsibilities include defining AI research strategy, developing novel reasoning and summarization approaches, selecting model architectures and deployment strategies, building large-scale production systems, mentoring scientists, establishing evaluation standards, and guiding cross-functional technical initiatives. The role also represents the company through publications, speaking engagements, and technical thought leadership.

Own enterprise technical support cases from triage through resolution across Kubernetes, cloud infrastructure, ML platforms, authentication, data connectivity, and model deployment. Diagnose issues, write actionable Jira bug reports, create knowledge-base documentation, coordinate global case handoffs, and communicate clearly with customers. The role includes live troubleshooting sessions and participation in an EMEA weekend on-call rotation.

Lead a 5–10 person team responsible for intelligence gathering, data analysis, and strategic decision-making. Set team goals, manage budget and resources, ensure deadlines, design training, coach members, mitigate security risks, and drive cross-functional collaboration to deliver high-quality data products and insights.

Maintain and transform investment data by building data systems and pipelines, performing complex analysis, combining multiple sources, improving data quality, and presenting results via visualization. Collaborate with clients, system architects, and product teams to deliver actionable insights and analytical tools for data-driven decisions.

The Enterprise Account Executive will drive new enterprise revenue, engage with senior decision-makers, and execute growth strategies for Data and AI training solutions.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

As the Director of Data Science, you'll mentor teams, oversee projects, drive data-driven decisions, and implement AI and ML solutions while enhancing our data pipeline.

Lead applied AI research and architecture for GenAI in cybersecurity: design LLM architectures, implement production-grade deep learning systems at scale, mentor scientists, set technical standards, and align AI strategy with business goals.

Serve as the technical authority for enterprise customers in data security, owning the technical sales process. Deliver technical presentations, demos, and whiteboard sessions, advise on AI/ML-driven data protection, and drive outcomes through value-based selling with partners and GTM teams.

Lead end-to-end delivery of complex technical projects across infrastructure, platforms, applications, data, cloud and security. Manage plans, budgets, risks, governance and stakeholder communications; coordinate technical teams to ensure timely, secure, high-quality outcomes while supporting benefit realisation and delivery practices.

Performs setup, installation, calibration, documentation, and operation of analog and digital instrumentation and data acquisition systems. Investigates and documents system malfunctions, assists with test-data interpretation and processing, and communicates with customers and support teams. The role requires weekend, second-shift, and overtime availability, occasional lifting of up to 50 pounds, and compliance with U.S. export-control requirements.

Serve as owner's representative for hyperscale data centre projects across pre-construction, construction and close-out: lead master schedule and budget creation, support contractor procurement and contract negotiations, manage general contractors and owner-direct vendors, coordinate value engineering, monitor budget and schedule, and drive close-out documentation and authority inspections.

Implement, audit and troubleshoot GA4 and Google Tag Manager setups, manage conversion tracking across major ad platforms, maintain server-side tracking, diagnose data quality issues, and build client-facing reports and dashboards. Handle ad-hoc tickets and client-facing delivery across DTC and B2B eCommerce accounts.

Lead and oversee EHS implementation and performance for a major data centre construction project in the Nordics. Manage site EHS procedures, audits, inspections, incident investigations, contractor compliance, performance indicators, and EHS-related performance evaluations to protect personnel and the public.

Lead the technical direction and standards for Azure data engineering. Design and assure scalable, secure pipelines and lakehouse architectures, review production changes in Azure DevOps, conduct proof-of-concept development, and research new platform capabilities. Collaborate with data scientists, architects, developers, platform teams, support teams, and external partners to ensure reliable, governed data solutions and smooth transition into support. The role provides hands-on technical leadership without direct line management.

Define, set up and ingest external clinical data streams (labs, ECG, imaging, EHR, wearables, genomics). Plan set-up activities, create trial-specific Data Transfer Agreements, translate medical concepts into data mappings, perform UAT, implement transfers into the CDR, monitor data quality and resolve provider conformance issues while ensuring regulatory and data protection compliance.

The Data Scientist (Product Analytics) will develop product analytics, improve data quality, conduct A/B tests, analyze user behavior, and collaborate with cross-functional teams to inform product strategies and enhance user engagement.

Lead a specialist BPA data team to manage end-to-end pension scheme data for buy-in/buy-out transitions. Ensure data accuracy, validation, reconciliations, benefit specification oversight, governance, regulatory compliance, stakeholder integration, third-party data management, and drive automation and continuous improvement to support successful scheme migrations.

Support implementation of data governance frameworks across Pensions & Savings. Work with Data Owners, Stewards, and Risk to monitor data quality, track governance actions, maintain documentation, and produce regular and ad-hoc reports to support regulatory compliance and governance maturity. Contribute to continuous improvement of data governance processes and stakeholder reporting.

Support data-led supervision of investment funds by analyzing large datasets, identifying trends and risks, building analytical solutions and Tableau dashboards, and communicating insights to guide regulatory decisions. The role collaborates with supervisors, fund managers, Authorisations, and Policy teams; responds to emerging fund-sector issues; builds stakeholder relationships; and contributes to improved conduct and customer outcomes across asset management.

Leads the development of integrated planning data models, standards, dashboards, and management information. Assesses business plans for affordability, capacity, deliverability, risks, and dependencies, translating complex information into decision-ready insight for senior leaders. Partners with Finance, People, Risk, Technology, and Planning teams to improve data quality, governance, and joined-up decision-making. Provides technical leadership, coaching, quality assurance, and continuous improvement without formal line management responsibility.

Leads data engineering teams building scalable data pipelines, lakehouse architectures, and data products on Databricks and AWS. Oversees hiring, coaching, delivery planning, architecture, platform reliability, SLAs, incident response, monitoring, and cost optimization. Partners cross-functionally with architects, product managers, analytics, and engineering teams to establish technical strategy, governance, and operational excellence.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.