Top Data Jobs in Edinburgh

Leads a Data Products team designing, developing, and evolving foundational data products for energy-market modeling, analytics, and client-facing systems. Owns data product quality, reliability, governance, schemas, versioning, metadata, and data contracts. Provides technical leadership, collaborates with subject matter experts and platform engineers, establishes engineering best practices, and supports agile delivery and continuous improvement.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Leads client engagements delivering data, analytics, automation, and AI solutions for complex programmes and business operations. Responsibilities include defining requirements, designing data processes and pipelines, resolving data quality issues, providing technical delivery assurance, applying generative AI and LLM solutions, managing stakeholders, supporting proposals, and mentoring teams. The role also contributes to reusable methods, documentation, innovation, and client relationship development.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Manage international third-party data providers supporting identity verification, fraud prevention, KYC/eKYC, document verification, and compliance products. Responsibilities include vendor sourcing, due diligence, onboarding, contract and usage documentation, data quality and performance evaluations, integration coordination, issue resolution, renewals, cost tracking, and process improvement. Collaborate with Product, Data Science, Engineering, Legal, Compliance, Procurement, and external partners across global markets.

Own Civica’s enterprise data and integration architecture strategy, defining current and future-state architectures, scalable data models, integration patterns, governance standards, and migration strategies. Lead architectural direction across ERP, professional services automation, data transformation, and integration programs. Advise executives and partners, support data security and compliance, and establish enterprise-wide master data management and single-source-of-truth foundations for analytics, automation, and AI.

Lead SFC’s data architecture function by defining enterprise and domain data models, architecture principles, standards, governance, and design assurance. Provide technical leadership on cloud data platforms, integration, interoperability, metadata, lineage, and master data. Advise senior stakeholders, manage architectural risks and technical debt, build consensus across teams, and coach colleagues to improve data architecture capability and adoption.

Leads complex enterprise customer engagements as a senior technical authority for data and AI architecture. Drives Databricks adoption, consumption strategy, executive relationships, competitive positioning, and large-scale architecture initiatives including migrations, real-time systems, lakehouse platforms, and AI/ML. Orchestrates cross-functional teams, mentors solutions architects, influences senior stakeholders, and contributes to technical thought leadership.

Own production NLP and applied AI systems that enrich scientific literature with entities, classifications, claims, and summaries. Design and evaluate classical NLP, embedding, retrieval, and LLM approaches; build scalable Python pipelines; manage cost, concurrency, reliability, and model quality. Collaborate with data engineers on Airflow and Dagster orchestration, contribute to agentic AI applications, and work with editors, product managers, and engineers to deliver research intelligence to users.

Build and operate NLP enrichment pipelines over scientific literature, extracting entities, classifications, claims, and summaries. Compare classical NLP, retrieval, embedding, LLM, and fine-tuned model approaches using evaluations, cost, quality, and operational metrics. Create golden datasets, define evaluation strategies, contribute to agentic AI applications, and collaborate with editors, product managers, engineers, SMEs, and vendors to deliver production modeling systems.

Build Paddle’s data science function and production decisioning systems from the ground up. Prioritize high-value use cases, personally deliver the first ML or agentic system, and own deployment, monitoring, experimentation, reliability, governance, and financial impact. The role will establish the production ML stack, collaborate across Product, Engineering, Risk, Finance, and Compliance, then hire and lead data scientists and ML engineers. Experience with regulated transactional domains, production ML operations, experimentation, and executive stakeholder management is required.

Owns the integration and analysis of people, security, technology, fraud, physical security, investigation, and governance data to identify insider-risk patterns. The role produces actionable intelligence, supports investigations, strengthens controls, and develops monitoring, automation, governance, and reporting capabilities. It requires influencing senior stakeholders, translating complex findings into business outcomes, and making risk-based recommendations amid ambiguity.

Delivers data governance initiatives by gathering requirements, documenting processes and data flows, supporting Microsoft Purview adoption, and improving data quality. Works with data owners, stewards, technical teams, and senior stakeholders to define governance processes, metadata standards, business glossaries, ownership models, controls, and quality metrics. Facilitates workshops, translates business needs into requirements and user stories, investigates data issues using SQL and profiling outputs, and communicates risks, dependencies, and decisions.

Analyze transactional data to detect and prevent financial crime, manage AML alerts, build SQL-based models and dashboards, and improve transaction monitoring and customer risk-rating controls. The role owns financial crime operations capacity planning, models scenarios, identifies data-led improvements, and presents insights to business leaders while collaborating with Product, Compliance, and cross-functional stakeholders.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.

Performs data-heavy functional, regression, and User Acceptance Testing for Finance change initiatives. Designs test cases from requirements and acceptance criteria, validates data using complex SQL and Excel, logs and tracks defects, supports issue resolution, and reports testing progress. Works closely with subject matter experts, business users, and delivery teams in a regulated banking environment.

Lead definition, monitoring and reporting of Group financial crime KRIs and tolerances. Drive data quality, investigations and MI for AML, sanctions, fraud and CDD. Partner with technology and business units to maintain data infrastructure, implement automated reporting, enhance controls and deliver dashboards and governance across the organisation.

Leads clinical data management across global trials from study start-up through database lock. Responsibilities include EDC builds, system integrations, UAT, data review and reconciliation, vendor oversight, regulatory compliance, risk mitigation, and stakeholder management. The role serves as the primary data lead for complex studies and requires expertise in CDISC standards, SDTM, ICH-GCP, clinical trial processes, and multiple clinical data systems.

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.

Design, build, and own scalable data pipelines from ingestion through analytics and incentive payouts. Lead data platform architecture, including Snowflake, dbt, Iceberg, and S3 workflows. Establish data quality, lineage, observability, performance, cost, and SLA standards. Partner with engineering and analysts to support telematics accuracy and driver incentive reporting. Develop maintainable data models and transformation layers while using AI coding tools to improve development speed and quality.

Build and evolve CluePoints’ enterprise data platform using Microsoft Fabric and Azure. Responsibilities include migrating Azure SQL and ADF workloads, integrating SaaS and web data, developing scalable ingestion and transformation pipelines, maintaining Kimball dimensional models, and establishing CI/CD, data quality, lineage, security, and monitoring. The role partners with analytics leadership and supports trusted Power BI reporting across multiple business functions.

Own and develop the company’s Power BI reporting environment, including semantic models, dashboards, advanced DAX, Power Query transformations, governance, deployment, and performance optimization. Collaborate with Data Engineering and business stakeholders to define KPIs, validate data, improve reporting quality, and deliver scalable BI solutions. Support data architecture, data quality, ad hoc analysis, and continuous improvement across enterprise reporting assets.

Define and govern ABB’s enterprise-wide data and information architecture. Establish data domains, models, standards, interoperability patterns, and governance processes while enabling lakehouse, data mesh, analytics, and AI initiatives. Ensure compliance with privacy, security, and data sovereignty requirements. Collaborate with architects, platform teams, and business stakeholders across a decentralized organization, guide adoption, support architecture reviews, and coach domain and solution architects.

Build and maintain cloud software for a Customer Data Platform, delivering complex features from design through production. Lead technical delivery, design scalable solutions, improve code quality, testing, observability, reliability, and security. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and roadmap discussions, and mentor engineers.

Build, maintain, and release reliable cloud software for the Customer Data Platform. Lead complex feature delivery, make architectural decisions, improve code quality, testing, observability, security, and operational reliability. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and OKRs, and mentor engineers through pairing, reviews, and technical guidance.