REPORTING AND ANALYTICS LEAD

Job Title: REPORTING AND ANALYTICS LEAD

Purpose of the role

• The opportunity is to build the organisation's engine for decision advantage turning data into actions, and actions into measurable outcomes. You will lead the portfolio of high-impact AI and decision science use cases, establish a disciplined delivery model, and ensure the function consistently converts business priorities into scalable solutions.
• You will create the end-to-end capability: from identifying the right problems, to building decision engines and predictive models, to deploying them with strong governance, monitoring, and adoption. This includes setting the standards for model quality, explainability, and responsible AI so leaders can trust the outputs and use them confidently in operational and financial decision-making.
• Over time, you will enable a shift from intuition-led choices to model-informed decisions across workforce planning, cost optimisation, performance management, and service delivery. Success in this role will be visible through sustained value delivery, faster decision cycles, stronger financial outcomes, and a mature AI capability that becomes a core part of how the business operates.
  
  

In This Role, You Will

• The candidate will deliver on the implementation of the framework through following activities below. Lead the design and delivery of decision science solutions including decision engines, forecasting, optimisation, and ML-based prioritisation models. Drive frameworks for demand elasticity, volumetric demand forecasting, capacity planning, and resource supply optimisation. Ensure models are explainable, auditable, and business-ready with clear decision logic and traceability.
• Build and manage a portfolio of AI use cases across operations, finance, risk, customer, and service functions. Ensure solutions move beyond POCs into production-grade AI products with clear ownership, monitoring, and ROI. Partner with technology teams to standardise model deployment, MLOps, model monitoring, and lifecycle management. Ensure AI solutions are grounded in trusted data (golden sources) with strong lineage, controls, and quality. Establish standards for metrics definition, semantic layers, and consistent business reporting. Drive governance for model risk, data privacy, and ethical AI in partnership with risk/compliance teams.
• Partner with senior stakeholders to translate business problems into measurable AI opportunities. Lead change management for adoptiontraining, communication, workflows, and business process integration. Track benefits through robust value measurement: savings, productivity, risk reduction, and service uplift.
• Build and lead a high-performing team across decision scientists, data scientists, analytics engineers, and AI engineers. Develop talent, career paths, and technical standards across the analytics lifecycle. Promote best practices in experimentation, statistical rigor, ML engineering, and storytelling.
• Manage a cross-functional team including business stakeholders, developers, and two strategic external suppliers. Set clear development priorities, performance expectations, and accountability measures for supplier teams. Proactively manage risks, issues, and changes in the scope, ensuring alignment with business objectives. Report regularly on project status, update Jira, Confluence, prepare the project plan. Provide mentoring and guidance to the Team members. Foster collaboration and accountable team culture focused on delivery excellence.
• Establish an operational performance framework for AI products including service reliability, monitoring, retraining triggers, model drift detection, and performance SLAs. Ensure AI solutions are production-ready with clear ownership, BAU support models, runbooks, and escalation paths. Define and implement model governance standards: documentation, validation, approvals, audit traceability, and periodic reviews.
• Embed responsible AI practices including explainability, fairness considerations, data privacy controls, and adherence to internal risk and compliance requirements. Drive continuous improvement through feedback loops, adoption tracking, and iterative enhancement of models and decision engines to sustain value over time.
• Act as a senior partner, building confidence in the CoE and ensuring strong sponsorship for priority initiatives. Lead cross-functional alignment on priorities, sequencing, and trade-offs across the AI portfolio, balancing value, feasibility, and delivery capacity. Influence senior stakeholders through strong storytelling, transparent reporting, and credible decision recommendations grounded in data. Manage stakeholder expectations by clearly communicating model limitations, assumptions, risks, and governance requirements. Build strong partnerships with technology and data platform teams to ensure solutions are scalable, secure, and aligned to enterprise architecture.
• Programme is highly complex, including: a diverse matrix, senior stakeholders, substantial work-streams. Managing across a wide variety of differing core processes and data technologies. Need to be able to work at multiple levels of the organization: senior management to ensure alignment and support for the program as well as project teams responsible for execution and delivery.
• To be successful in this exciting role, you should possess a combination of technical skills, analytical acumen, and collaborative abilities. Here are the key attributes and qualifications needed:
  
  

Qualification

• Master's degree in mathematics, Business, Computer Science, Engineering, or a related fields. 12+ years in analytics, data science, decision science, or AI product leadership. Python, Machine learning and AI certifications are a must. Internal candidates must have completed all the AI ambassador courses in degreed.
• Proven experience building and scaling an analytics/AI CoE or equivalent enterprise capability. Track record of delivering production-grade AI solutions with measurable business outcomes. Experience working in complex matrix organisations with strong stakeholder management.
• Experience with workforce planning, resource optimisation, or operational cost modelling. Experience in finance, banking, shared services, or global operations environments. Familiarity with Responsible AI, model risk frameworks, and regulatory expectations. Experience deploying LLM-based analytics solutions (e.g., RAG, conversational BI) with guardrails.
• Strategic thinking with a hands-on approach to delivery. Excellent communication and negotiation skills. Strong analytical and problem-solving ability.
• Leadership and team-building mindset. High attention to details and organizational skills. Comfortable with ambiguity and building from scratch
• Strong governance mindset (trust, controls, auditability) By combining these skills and attributes, a candidate can effectively contribute to the department's mission of enhancing operational excellence and informed decision-making within the organization.
  
  

Hsbc.COm/Careers

You'll achieve more at HSBC

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.

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