Head of BR Risk Analytics

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HSBC is one of the largest banking and financial services organisations in the world, with operations in 62 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.

We are currently seeking an experienced professional to join our team in the role of Head of BR Risk Analytics.

Role Purpose:

• Lead the GCOO Business Risk Analytics function, designing and delivering a modern, scalable, data-led Risk & Control reporting utility that materially improves executive decision-making and delivers measurable risk outcomes.

Principal Responsibilities:

• Own the end-to-end transition from semi-automated, Qlik-based MI reporting to an AI-first insight capability—embedding advanced analytics and intelligent automation into the core design and day-to-day operating model, underpinned by robust data lineage, transparent auditability, and trusted, timely decision-grade insights.
• Bring deep Risk & Control domain leadership (RCA, control testing/assurance, issues management, KRIs/KPIs) and translate these mechanics into trusted, decision-useful enterprise MI that strengthens risk governance, supports risk appetite monitoring and enables safe, secure services for customers, colleagues and the Group.
• Define and deliver the target state for analytics architecture for BRR reporting, working in partnership and alignment with the Data Analytics Office and Technology. Be accountable for roadmap, benefits, and adoption—ensuring responsible AI through model governance, explainability, monitoring, and appropriate human oversight to improve reporting standards, deepen risk/control insights, and drive efficiency.

Key Accountabilities:

Strategy, Roadmap & Operating Model:

• Set and own a multi-year Risk & Control analytics strategy aligned to GCOO/BRR and Group objectives, with clear outcomes, milestones and investment cases.
• Use the 4Qs to prioritise demand and investment (customer benefit, simplicity/agility, stands the test of time, aligned to Values and risk appetite).
• Define future state BRR model target using reusable datasets/semantic layers, metadata/lineage, data quality controls and retention.
• Establish an embedded “MI utility” operating model (service catalogue, SLAs, demand intake, prioritisation, run/change model) to drive standardisation and reuse across GCOO and more widely, as appropriate.
• Run a continuous improvement cadence (feedback loops, iteration cycles, adoption metrics) to ensure MI remains relevant, used and decision-impacting.

Risk & Control Domain Leadership:

• Promote a speak-up culture and constructive challenge on risk signals, thresholds and emerging issues; ensure timely, evidence-based escalation.
• Provide subject matter leadership across Risk and Control, operating effectiveness, issues management, KRIs/KPIs and risk appetite monitoring.
• Translate Risk & Control requirements into clear MI definitions, measurement logic, thresholds and governance so reporting is comparable, decision-useful and audit-ready.
• Ensure MI supports effective oversight of controls across GCOO and partner business areas (e.g. GCIO & IWPB).
• Shape MI to support executive decisioning through clear narratives, drivers, forward-looking indicators and actionable insights—focusing on customer impact and service resilience

Operationalised MI, Automation & Data Engineering:

• Design and deliver automated MI utilities, including data pipelines, curated/reusable datasets, semantic layers, dashboards and self-serve reporting—reducing manual effort and duplication.
• Implement robust data quality management (controls, reconciliation, exception handling), lineage and auditability to meet internal governance and regulatory expectations.
• Drive simplification and standardisation to improve timeliness, moving towards timely/near real-time MI where feasible and appropriate.
• Embed MI into BAU through repeatable production processes (release/change control, monitoring, incident management, clear ownership) and measurable adoption in governance rhythms.
• Inspect what you expect by defining and tracking MI quality KPIs and usage metrics

AI-Enabled Insight:

• Use AI to reduce manual effort and accelerate insight generation while maintaining explainability and human oversight.
• Identify, prioritise and deliver governed AI use cases that improve risk insight and operational efficiency (e.g., automated insights/narratives, anomaly detection, control monitoring, early-warning indicators).
• Ensure AI adoption is safe and compliant (privacy, security, model risk management, explainability, human oversight and approvals in line with Group governance).
• Partner with BRR, Infrastructure and Technology teams to accelerate delivery through reusable patterns, tooling and capability uplift.

Governance, Risk Management & Stakeholder Partnership:

• Continually reassess operational risks relevant to analytics services and adapt to regulatory, economic, organisational and process changes.
• Build strong relationships across GCOO, Global Businesses and Infrastructure teams, as well as principal subsidiaries; manage internal customer experience and resolve root causes of dissatisfaction/SLA breaches.
• Drive adoption and behavioural change so MI is consistently used in governance forums, risk appetite monitoring and prioritisation decisions.

Requirements:

Leadership & People:

• Ensure MI drives measurable improvements in customer outcomes, service resilience and executive decision-making.
• Standardise data, definitions and reporting to reduce complexity and accelerate delivery (“progress beats perfection”).
• Create constructive challenge, align stakeholders quickly, secure clear decisions and drive committed follow-through.
• Build a diverse, multi-skilled team across Risk & Control SMEs, analytics, visualisation and AI; set clear expectations and role-model inclusive leadership.
• Coach and develop talent, build succession and a high-performance learning culture; uplift data/AI capability through hiring, coaching and communities of practice.
• Act as Business Risk site lead in Hyderabad, participating in local governance as required.

Measurable Outcomes (Owned by the Role):

• Simplify risk reporting and decision-making to enable faster, safer execution that protects customers and strengthens trust (e.g., reduced cycle time, fewer manual touchpoints, fewer data defects/rework).
• Reduce operating cost over the medium term through automation, standardisation and reuse.
• Increase reuse of curated datasets/semantic layers and automated controls monitoring; improve auditability (lineage, evidence, consistent definitions) and stakeholder satisfaction.
• Demonstrably improve executive decisioning (e.g., increased adoption in governance forums, reduced time-to-decision, clearer prioritisation of remediation and control uplift).
• Improve risk outcomes through earlier detection and intervention (e.g., fewer repeat issues, improved control effectiveness trends, reduced severity/ageing of issues, stronger risk appetite monitoring).

Essential Skills & Experience:

• Deep expertise in enterprise risk & control mechanics (RCA, control testing/assurance, issues management, KRIs/KPIs) with proven ability to translate these into executive-grade, decision-useful enterprise MI.
• Proven track record designing and delivering reporting/MI utilities and automation (data pipelines, reusable datasets/semantic layers, dashboards, controls monitoring) that materially reduce manual effort and improve data quality, lineage and auditability.
• Demonstrable capability to operationalise, iterate and embed MI into BAU governance and decision-making (adoption metrics, continuous improvement cycles, stakeholder routines, measurable decision/risk impact).
• Demonstrated leadership of strategy and transformation in a global, matrixed organisation (target architecture, data strategy, tooling selection, governance and operating model design).
• Practical AI fluency with evidence of delivering governed AI use cases (automated insights/narratives, anomaly detection, control monitoring) with appropriate privacy, security and model governance.
• Strong stakeholder management skills: able to operate as a trusted adviser to senior leaders and translate business outcomes into clear, executable delivery plans.
• Strong risk management mindset with disciplined execution, issue resolution and adherence to regulatory expectations and internal governance standards.
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