The Head of Business Analytics leads DEL's analytics capability, using data, insights and evidence to identify opportunities, solve business problems, improve customer experiences, and support better decision-making across product, process and service design. The role partners closely with Business Engineers and stakeholders to quantify problems, measure outcomes and ensure solutions deliver meaningful business value.
The role also drives continuous improvement through process intelligence, analytics and AI, identifying optimisation, simplification and automation opportunities while establishing the measurement frameworks needed to track performance, validate impact and sustain results.
Areas of responsibility may include but not limited to
Analyses and presents data in a way that provides actionable insights, by presenting findings in a practical and interpretable way for the business.
Identifies key trends in large datasets and interacts with business to understand the impact of these trends and identifies actions to mitigate or take advantage of the trends identified.
Identifies appropriate models / algorithms to mine large datasets and develop rich insights into customer behaviour.
Build analytics assets and processes to ensure repeatable results and enable automation and reuse.
Drives implementation on key projects identified through analytics by engaging relevant stakeholders, ensuring projects progress as planned and conducting supplementary analysis for decision making.
Responsible for working with and growing a team of analytics professionals.
Personal Attributes and Skills
Strategy and leadership: Define and lead the Business Analytics strategy and operating model, aligned to Bank and DEL priorities, while building and leading a high-performing team.
Proactive insight and measurement: Use customer, process and operational data to proactively identify opportunities, establish performance baselines, and strengthen data-driven decision-making.
Process and journey analytics: Analyse processes and customer journeys to identify inefficiencies, gaps and improvement opportunities, supporting simplification, automation and optimisation.
Analytical products: Develop and manage reusable dashboards, models and analytical tools that provide insights into customer behaviour, operational performance and business outcomes.
AI adoption and enablement: Drive AI adoption by embedding AI tools into analytics and design activities, identifying use cases, building capability and measuring value delivered.
Prioritisation and governance: Prioritise high-value strategic analytics initiatives, leveraging AI and self-service capabilities, while ensuring data quality, governance and compliance.
The successful candidate will demonstrate:
A proactive, curious and opportunity-led mindset, with the confidence to challenge assumptions constructively and an early-adopter approach to AI-enabled ways of working.
Strong strategic, commercial and analytical thinking, including problem framing and hypothesis development, excellent attention to detail, and a passion for using evidence to deliver measurable improvements.
Sound judgement in ambiguous, early-stage design environments and when prioritising competing demands.
Strong collaboration, influencing and communication skills, a collaborative leadership style, a commitment to continuous learning, and high levels of integrity and accountability.
Knowledge and Skills
Essential
Advanced analysis of complex datasets, strong SQL or Databricks skills, and advanced Power BI (or equivalent), Excel and PowerPoint, with strong visualisation, storytelling and executive communication skills.
Strong understanding of end-to-end processes, customer journeys, hypothesis development and experimentation, and the ability to translate insight into practical design or business recommendations.
Practical experience using AI-assisted analytics and generative AI tools such as Microsoft Copilot, with a sound understanding of responsible AI principles.
Strong stakeholder engagement, influencing and demand-management skills, and a proven ability to lead a professional team.
Advantageous
Process mining or advanced process analytics and experience supporting product, process or customer-experience design using human-centred design principles.
Statistical hypothesis testing, experimental design, predictive analytics and machine learning, using statistical or advanced modelling tools.
Experience leading AI adoption or piloting agentic AI use cases, and in operational improvement, digital product development or transformation, with knowledge of retail banking products and processes.
Qualifications and Experience
A relevant degree in Informatics, Data Science, Statistics, Economics, Mathematics, Finance, Computer Science, Engineering or another relevant analytical discipline.
A postgraduate qualification in analytics, data science, business, engineering or a related discipline.
Relevant process-improvement, analytics, data, AI or leadership accreditations, would be advantageous.
5 - 8 years' experience in business, data or process analytics, including leading an analytics team or capability.
5 - 8 years' experience partnering with multidisciplinary teams and senior stakeholders to turn insight into business, product, process or customer-experience decisions and measurable outcomes, using reusable products, automation and AI to reduce once-off analysis.
3 - 5 years' experience in digital banking, financial services, product design, operational improvement or customer journey analytics would be advantageous; retail banking experience is preferred.