Top Data Jobs in Edinburgh

Analyse membership and CRM data to produce insights that inform engagement and membership strategy. Source and manipulate data from multiple systems, run statistical analyses, deliver regular and ad-hoc reports, create visualisations, present findings to technical and non-technical stakeholders, and contribute to cross-functional projects to improve member outcomes.

On-site mechanical quality coordinator for a large data centre construction project: oversee mechanical construction, review and approve ITPs and checklists, verify works against standards, develop QA/QC checklists in BIM, witness commissioning (HVAC, pumps, piping, fire protection), ensure contractors complete BIM 360 records, verify calibration and as-built/red-line accuracy, escalate and drive resolution of quality issues, and promote continual improvement.

Design, build, and maintain scalable analytics models and automated reporting. Partner with Sales, Product, Marketing, and Finance to define KPIs, deliver ad hoc analysis, improve data quality and observability, and establish analytics engineering best practices to enable self-service analytics.

Perform high-level video captioning, action-based timeline segmentation, and visually-verifiable action labeling. Review and correct AI-generated captions, meet accuracy benchmarks, follow strict guidelines, and complete certification.

Remote Arabic-speaking data annotator responsible for accurately labeling text, audio, and images in Arabic and English or French. Follow detailed guidelines, validate existing annotations, and meet productivity and quality targets while collaborating with the team to support ML/NLP model training.

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.

Design, build and optimise scalable cloud-native data platforms and pipelines using Microsoft Fabric, Azure Databricks, Synapse and ADF. Implement ETL/ELT with PySpark and SQL, ensure data governance, performance, reliability, and collaborate with data scientists and engineers to deliver production solutions.

Ensure quality, consistency, and governance of enterprise master data through data analysis, reporting, and collaboration. Perform data mining, identify quality issues, develop dashboards, run system queries, maintain data, support supplier outreach, and meet SLAs while supporting business initiatives and process improvements.

Design, build, and maintain scalable data pipelines and Airflow orchestration; deliver Databricks-based data engineering solutions; support data science workflows; collaborate via GitHub, code reviews, and shared engineering standards; ensure reliable, observable, and maintainable pipelines.

Design, build and maintain scalable data pipelines and Airflow orchestration; deliver Databricks-based data engineering solutions; collaborate via GitHub, support data science workflows, and promote observable, maintainable production delivery.

Lead product-focused data science work: run quantitative analyses and large-scale A/B tests, enable product teams with measurement, drive lifetime value analysis with finance, and partner with engineers to ensure correct data collection and actionable insights.

Lead data strategy and standards across product teams, develop experimentation best practices, mentor data scientists and analytics engineers, collaborate with product/engineering/design/research, and apply advanced data techniques to improve user engagement and product decision-making.

Lead commercial management, governance and optimisation of market data services across the organisation. Manage supplier relationships, contracts, licensing, inventory and usage controls, drive cost optimisation, ensure compliance, partner with Investment, Finance, Procurement and Change teams, and shape market data strategy and operating model.

The Senior Data Scientist will develop and test deep learning architectures, drive strategy across teams, and ensure models effectively solve real-world financial problems while mentoring others.

The Forward Deployed Data Solution Engineer works embedded within teams to ensure software quality, guide technical decisions, and design integration patterns, focusing on AI-enabled capabilities and data governance.

Lead delivery and ongoing ownership of a modern, cloud-based data platform. Define migration and integration roadmaps, manage programme delivery, embed data governance, support data engineering teams, drive adoption, and optimise reporting, analytics and data quality to enable business decision-making.

Manage client relationships as a subject matter expert, collaborating with Account Executives to drive revenue, engage clients, and provide product insights for innovative solutions.

Manage the full commercial lifecycle for enterprise and strategic customer accounts: renewals, pricing, contract amendments, upsell strategy, and expansion. Build bespoke commercial strategies using usage data and market context, forecast renewal pipeline, lead commercial QBRs, negotiate with senior stakeholders and procurement, and partner with Customer Success and solution consultants to quantify ROI and drive retained and expanded ARR.

Abstract and enter clinical data from healthcare records, apply clinical operations policies, troubleshoot VPN/MFA access, maintain confidentiality, and provide guidance to less-experienced team members while ensuring data quality and workflow adherence.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design, develop, and deploy machine learning and LLM-based solutions for risk scoring, compliance prediction, churn analysis, and contractor analytics. Integrate models into production on AWS and Azure, use Microsoft Fabric for data pipelines and feature engineering, implement model monitoring and retraining, and collaborate cross-functionally within an Agile SDLC.

Lead and define the data science and AI strategy, deliver production-grade ML and generative AI solutions, operationalise models with MLOps and governance, build and mentor a high-performing team, and influence stakeholders to drive measurable business impact.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Design, build, and maintain scalable cloud-based data platform and ETL pipelines on Azure. Develop data models, ensure data quality, optimize performance, implement governance and security, and collaborate with stakeholders and data scientists to enable data-driven solutions.