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 a team overseeing operational risks across technology, cyber, and data. Responsibilities include allocating resources, reviewing risk assessments, testing controls, monitoring threats, evolving risk policies and appetites, preparing executive reporting, and representing the oversight function at governance committees. The role acts as deputy to the Head of Operational Risk Oversight and provides independent challenge, strategic direction, team development, and assurance to senior management.

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.

Leads data governance portfolios, frameworks, policies, controls, data ownership, quality, metadata, lineage, and assurance activities. Embeds governance across data products, platforms, analytics, and AI initiatives; investigates quality issues; develops reporting and remediation plans; influences senior stakeholders; and mentors a sub-team while improving repeatable governance methods.

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 Software Engineer will develop data pipelines, manage cloud infrastructure, and collaborate with AI scientists to enhance data acquisition for model training.

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.

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 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.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Designs, builds, operates, and supports secure, governed data pipelines and services for reporting, integration, migration, and analytics. Responsibilities include ingestion, transformation, validation, publishing, data modeling, quality controls, lineage, metadata, access controls, migration, monitoring, troubleshooting, and documentation. The role collaborates with analysts, architects, developers, and business stakeholders across cloud data environments.

Leads engineering strategy, architecture, delivery, and productionisation for a secure cloud Data Intelligence Platform. Designs ingestion, streaming, transformation, lakehouse, serving, API, and feature-pipeline capabilities while establishing data quality, governance, lineage, observability, CI/CD, infrastructure-as-code, resilience, and FinOps standards. Coordinates analytics, ML, security, infrastructure, suppliers, and operations; manages technical priorities and risks; supports governance decisions; and mentors data engineers.

Embedded within the commercial team, the Staff Data Analyst will own trusted metrics for conversion, contracted volume, sales, margins, pipeline, and forecasting. Responsibilities include improving forecast accuracy, diagnosing CRM and transactional data quality issues, analyzing commercial opportunities and partner performance, identifying risks and trends, and translating complex findings into recommendations for senior stakeholders. The role also promotes metric adoption and self-service analytics across non-technical commercial teams.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Leads the design and scaling of Databricks-based data, analytics, and AI platforms supporting global customer operations. Builds enterprise data pipelines, machine learning, NLP, generative AI, conversational AI, dashboards, and operational intelligence solutions. Establishes governance, data quality, CI/CD, reliability, and AI lifecycle standards while partnering with product, operations, engineering, workforce management, and AI teams. Provides technical leadership, coaching, and mentorship to engineers and data scientists.

Evaluate and annotate 3D meshes, GLB files, images, and short videos for model training and error resolution. Identify whether assets are artist-created or AI-generated, and detect geometry defects, topology errors, and scene inconsistencies. Requires hands-on 3D modeling experience, familiarity with Blender or Maya, strong mesh-topology knowledge, English proficiency, and a suitable computer with dedicated graphics capabilities.

Curate and interpret drug discovery data from scientific papers, patents, and other disclosures. Responsibilities include interpreting chemical structures and biological data, building and maintaining databases, researching biopharmaceutical R&D, and surfacing accurate insights for scientific and industry audiences. The role requires strong data curation, patent literature, chemoinformatics, scientific communication, and attention to detail skills.

Designs and delivers integrated customer data solutions supporting Thales UK’s digital and data strategy. Responsibilities include architecture design, data integration, proof-of-concept development, stakeholder engagement, technical documentation, data governance, solution oversight, and lifecycle management. The role supports UK Ministry of Defence data standards, collaborates with engineering and digital platform teams, evaluates emerging technologies, and develops secure data management and analytics solutions across complex defence environments.

Owns site maintenance planning, scheduling, work-order lifecycle management, CMMS administration, preventive maintenance programs, asset lifecycle planning, compliance controls, vendor performance, and KPI reporting. The role coordinates engineering teams, vendors, stakeholders, and global operations to maintain uptime, ensure audit readiness, implement change controls, and drive data-led maintenance improvements across data-center sites.

Lead monitoring, analysis and reporting of data quality for Collections & Recoveries. Identify root causes, manage remediation, produce Data Quality Scorecards, maintain governance artefacts (business terms, metadata, lineage, rules), run control checks, and support enterprise reporting governance while partnering with stakeholders and the Data Steward to improve data accuracy and compliance.