Senior Tech, Data & AI Auditor

The position is based either in London, Paris or Zurich (depending on the successful candidate’s location). The successful candidate will support the global GIA team based in the EMEA, UK, US and APAC regions. This is a unique and important role within GIA. The successful candidate will bring strong expertise in technology, data, AI development, testing, governance and risk management, helping GIA provide effective assurance over the organisation's expanding use of digital technologies and AI-enabled solutions.

The role has a dual focus: (1) supporting audits over technology, data, automation and AI-related risks, applications, processes and controls; and (2) helping to shape the future of GIA by developing and applying data analytics, automation solutions, and AI tools that enhance audit effectiveness, efficiency and insight generation.

This role is a strong accelerator to leverage and expand your expertise, tackle real business challenges, and deliver tangible impact. The successful candidate will work closely with both GIA teams and SCOR's wider Technology & Data communities, acting as a bridge between technical innovation and risk-based assurance.

Candidates may come from a variety of technology, data, AI or analytical backgrounds and, whilst internal audit or enterprise risk management experience would be advantageous, it is not essential. The successful candidate will be supported by experienced members of the GIA team, providing an opportunity to develop audit and assurance expertise alongside their existing technical skills. We are seeking curious, motivated individuals who are keen to apply their technology, data and AI skills in a new and evolving context while helping to transform the future of GIA.

This description takes into account the principal responsibilities and is not to be considered exhaustive:

Technology, Data & AI Assurance

• Support audits covering technology platforms, cloud environments, data management, automation initiatives, AI solutions and emerging technologies.

• Evaluate technology development, change management, model governance, AI lifecycle management, data quality and cybersecurity controls.

• Review AI solutions, machine learning models and intelligent agents for governance, accountability, explainability, fairness, transparency and regulatory compliance.

• Perform risk assessments and identify emerging technology and AI risks that should be incorporated into audit planning.

• Support auditors in evaluating complex technology-related control environments and translating technical concepts into business risks and audit conclusions.

• Contribute to the assessment of the design and effectiveness of governance, risk management frameworks, internal controls and oversight arrangements for technology and AI-related activities.

Data Analytics & Continuous Assurance

• Design and execute advanced data analytics to identify risks, anomalies, trends and control weaknesses.

• Develop automated testing and continuous auditing capabilities.

• Create dashboards, visualisations and data-driven insights to support audit planning, fieldwork and reporting.

• Promote the use of analytics across the audit lifecycle and build data literacy within the audit team.

Internal Audit Innovation & Transformation

• Identify opportunities to automate audit activities and improve audit productivity.

• Design, build and deploy AI-enabled tools, workflow automations and intelligent agents to support audit execution.

• Contribute to the development of the Internal Audit technology and innovation roadmap.

Stakeholder Engagement & Collaboration

• Partner with Technology, Data & AI teams to understand significant programmes, architectures, risks and control environments.

• Act as a subject matter expert for technology, data and AI matters across the Internal Audit team.

• Provide coaching and technical guidance to auditors in technology and AI-related areas.

• Support discussions with senior management, risk functions and governance committees regarding technology and AI risk management practices.

• Maintain awareness of relevant industry developments, regulatory expectations and emerging risks.

Required Experience

Candidates should possess several of the following:

• 3-5+ years’ experience in AI development, data engineering, data science, technology consulting, software engineering, automation, technology risk, IT audit or digital transformation.

• Practical experience designing, building, deploying, testing or reviewing technology, automation, data analytics or AI solutions.

• Hands-on experience with modern enterprise data and AI platforms, such as Databricks, Palantir Foundry, Microsoft Fabric, Azure AI, Azure OpenAI, Snowflake, Dataiku or equivalent.

• Practical knowledge of Generative AI, Large Language Models, Retrieval Augmented Generation, Agentic AI, multi-agent systems, machine learning or predictive analytics.

• Experience with AI and data solution lifecycle controls, including data quality, data lineage, model testing, prompt testing, access controls, monitoring, explainability, human oversight and governance.

• Strong data analytics capability, including Python, SQL, Spark, Power BI, Tableau or equivalent tools.

• Ability to translate complex technical concepts into practical business risks, control implications and clear audit conclusions.

Desirable Experience

• Internal Audit experience. Experience auditing technology, data analytics or AI environments.

• Insurance, reinsurance or financial services experience.

• Experience with AI governance, model risk management or responsible AI frameworks.

Required Technical Competencies

• Strong understanding of technology architecture, data management and modern software development practices.

• Working knowledge of AI, machine learning, Generative AI, Large Language Models (LLMs) and Agentic AI.

• Strong data analytics capability, including experience with platforms such as Python, SQL, Power BI, Tableau, Databricks, Palantir Foundry.

• Understanding of technology risks, cybersecurity principles and control frameworks.

• Ability to assess and test automated controls, algorithms and AI-enabled processes.

• Ability to design innovative technology solutions to improve business processes.

Required Personal Competencies

• Intellectual curiosity and passion for innovation.

• Strong problem-solving and analytical skills.

• Self-starter with the ability to work independently and manage multiple priorities.

• Ability to translate complex technical concepts into clear business insights.

• Collaborative, adaptable and delivery-focused.

• Growth mindset with a continuous learning orientation.

Education and Professional Qualifications

Bachelor's degree (or equivalent professional experience) in one or more of the following disciplines:

• Computer Science, Artificial Intelligence (AI)

• Data Science, Data Analytics

• Software Engineering, Computer Engineering, Data Engineering

• Information Technology (IT), Information Systems, Cyber Security

• Mathematics, Statistics

• Risk Management (Technology Risk focus)

Professional certifications may include:

• Databricks Certificates (e.g. Data Engineer, Machine Learning, Generative AI Associate or Professional)

• Palantir Foundry certification or equivalent platform experience

• Microsoft Certificates (e.g. Azure AI Engineer Associate, Azure Data Scientist Associate)

• AWS Certified Machine Learning Engineer

• Google Professional Machine Learning Engineer

• CISA - Certified Information Systems Auditor

• CAIP - Certified Artificial Intelligence Practitioner