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Lead financial risk transformation programs across liquidity, credit, and market risk, advising senior stakeholders and implementing enhanced controls.

The Principal Consultant will lead cyber and operational resilience transformations, working with financial institutions to implement strategies, assess risks, and enhance frameworks, focusing on regulatory compliance.

Deliver and manage client engagements in financial services: analyze current and future-state processes, liaise between business and technical teams, lead workstreams, produce high-quality consulting deliverables, and support large-scale digital, data, finance, and IT transformation projects on client sites.

Lead and coordinate end-to-end data migration from a legacy SaaS to a new platform: define migration strategy, validation and reconciliation processes, design reconciliation tooling, plan cutover and rollback, manage risks and stakeholders, and enable readiness for migration rehearsals and production cutover.

Lead technical teams to design, implement and maintain IT solutions for financial services projects. Define architecture, assign tasks, conduct code reviews, resolve technical issues, and liaise with clients and management to ensure quality and delivery. Provide technical guidance and keep solutions aligned with business goals and industry trends.

Lead AI-driven data and analytics programmes in financial services, mentoring teams and ensuring delivery of measurable outcomes.

The role involves managing financial reporting requirements including data assessment and transformation, stakeholder engagement, and ensuring regulatory compliance for reporting processes.

Lead client workshops, analyse current-state processes, identify gaps, define future-state solutions, gather and manage requirements, produce business cases, process maps, user stories and delivery roadmaps, and present recommendations while collaborating with consulting, technology, and delivery teams.

Lead business-facing data analysis to assess data models, identify duplicates and inconsistencies across sources, and recommend practical data quality and governance improvements. Run discovery sessions, present findings to stakeholders, produce documentation, roadmaps, and support prioritisation of data improvement initiatives to enable better business decisions.

Lead and mentor data science teams to design, develop, and deploy ML and AI solutions (including GenAI/LLMs). Align technical outcomes with business goals, drive MLOps and model governance, and deliver scalable production systems.