Job Summary

•    The Data & AI Lead is responsible for identifying, shaping, and delivering AI-enabled products that improve risk and compliance processes and deliver measurable business value.
•    The role sits within the AI Centre of Excellence (CoE) and acts as the bridge between business, data, AI engineering, and governance teams, ensuring AI solutions are aligned to strategic priorities, deliver tangible outcomes, and are successfully adopted by business users.
•    In addition to product ownership, the role drives process excellence and value realisation, ensuring AI initiatives generate measurable productivity, risk reduction, and operational efficiency improvements

Key Responsibilities

Strategy
•    Define and maintain the Data & AI capability strategy and product roadmap for Risk & Compliance, ensuring AI initiatives are aligned to business priorities, enterprise AI direction and measurable value outcomes.
•    Own the AI use case portfolio across assisted, enabled, generative and agentic automation, ensuring a coherent path from ideation to prioritisation, design, delivery, adoption, scale and retirement.
•    Partner closely with respective Product Owners, Business Owners and Process Owners to review the full product and process journey, identifying where AI, data, automation and analytics can materially improve productivity, risk outcomes, user experience and decision quality.
•    Lead structured opportunity discovery across products and processes to identify areas where process engineering through AI and data tools can deliver substantial qualitative and quantitative benefits, including cycle-time reduction, effort elimination, quality improvement, control uplift and improved decision support.
•    Continuously challenge existing ways of working and push the boundaries of imagination with business stakeholders by bringing practical ideas on GenAI, agentic AI, workflow automation, data intelligence and reusable AI capability patterns.
•    Support strategic buy, build, reuse or partner decisions for AI capabilities, ensuring investment is directed towards scalable, reusable and value-accretive solutions.
•    Maintain a centralised AI backlog and product pipeline that balances business value, feasibility, risk, complexity, user adoption, delivery capacity and strategic relevance.
Business
•    Act as the primary business-facing lead for AI capability development, building trusted relationships with Product Owners, Business Owners, SMEs and senior stakeholders to identify, shape and deliver AI-enabled business transformation.
•    Work with product and business owners to assess the entire product journey, including upstream inputs, workflow steps, decision points, controls, handoffs, pain points, downstream outputs and user experience

•    Translate business challenges into clear, outcome-led AI opportunities, ensuring each use case has a defined problem statement, target user group, expected benefit, success measure and adoption pathway.
•    Lead process and value analysis to determine where AI and data tools can generate measurable business impact, including productivity gains, cost avoidance, improved quality, better risk detection, reduced manual intervention and enhanced decision-making.
•    Quantify expected benefits before implementation and track realised benefits after deployment, covering both quantitative measures such as time saved, volume processed, cost avoided and cycle-time reduction, and qualitative outcomes such as improved user confidence, better insights, stronger controls and improved service experience.
•    Enable product and business owners to reimagine how work can be performed using AI, including GenAI assistants, copilots, agentic workflows, intelligent triage, automated summarisation, insight generation and decision-support tools.
•    Ensure AI products are business-relevant, user-centred, measurable, scalable and designed to deliver sustained adoption rather than isolated experimentation.
Processes
•    Establish and operate a structured AI product lifecycle covering intake, opportunity discovery, journey review, prioritisation, requirements definition, design, development oversight, deployment readiness, adoption, value tracking and continuous improvement.
•    Facilitate end-to-end product journey and process engineering reviews with Product Owners and Business Owners to identify friction, duplication, manual effort, control gaps, rework, delays and decision bottlenecks that can be improved through AI and data-enabled solutions.

•    Convert business and process opportunities into well-defined product artefacts, including product requirement documents, user journeys, process maps, feature definitions, prioritisation criteria, value hypotheses and delivery roadmaps.
•    Work closely with AI engineering, data and analytics teams, GenAI prompt specialists, governance teams and business SMEs to ensure AI solutions are designed and delivered against clearly defined business outcomes.
•    Oversee use case delivery from ideation through deployment, ensuring delivery progress, dependencies, risks, milestones, design decisions, readiness activities and business adoption plans are actively managed.
•    Define and maintain value measurement approaches, including baseline metrics, benefit assumptions, productivity scorecards, adoption dashboards, value realisation tracking and portfolio performance reporting.
•    Use feedback from users, adoption metrics, operational outcomes, process performance and governance inputs to continuously improve AI products, backlog priorities and product journey design.
People & Talent
•    Build and own strong working relationships with Product Owners, Business Owners, Process Owners, Technology, Data, Risk, Compliance and AI Governance teams to create a shared agenda for AI-enabled transformation.
•    Act as a trusted advisor to business teams by helping them understand what is possible with AI, GenAI, agentic AI, data products and automation, while translating ambition into practical, deliverable and governed use cases.
•    Facilitate ideation sessions, product clinics, journey walkthroughs and design workshops that encourage stakeholders to rethink how processes, controls and decisions could be redesigned using AI and data.
•    Build confidence and adoption across business teams by partnering with learning, engagement and change teams to deliver training, demos, playbooks, engagement plans and user enablement activities.
•    Promote a product and value-led mindset across AI delivery teams, ensuring teams remain focused on user outcomes, measurable impact, adoption, continuous improvement and responsible innovation.
•    Encourage a culture of curiosity, experimentation and disciplined execution, where teams are empowered to challenge legacy processes and explore ambitious but controlled uses of AI.
•    Support the development of AI fluency across business stakeholders by sharing reusable examples, emerging use cases, lessons learned and practical patterns for applying AI to real business problems.

Risk Management
•    Ensure AI use cases are shaped and delivered in line with enterprise AI governance, responsible AI, data, risk, compliance, security and regulatory expectations.
•    Work with Product Owners and Business Owners to identify risks across the full product journey, including process risks, control gaps, data risks, adoption risks, operational risks, model risks and inappropriate automation risks.
•    Partner with AI Governance, Risk, Compliance and assurance teams to ensure appropriate validation, explainability, bias assessment, control requirements, monitoring inputs and approval evidence are built into product delivery.
•    Ensure AI-enabled process engineering does not compromise control effectiveness, accountability, data integrity, customer outcomes, regulatory expectations or operational resilience.
•    Identify and manage delivery risks, dependency risks, value realisation risks, user adoption risks and operational readiness risks across the AI portfolio, escalating material issues where required.
•    Ensure AI products are implemented with appropriate human oversight, usage boundaries, business readiness, control documentation, monitoring inputs and feedback mechanisms to support sustainable and controlled use.
•    Use qualitative and quantitative performance insights to identify where deployed AI products may require refinement, additional controls, retraining, user education, redesign or retirement.
Governance
•    Maintain transparent governance over the AI product portfolio, including roadmap status, prioritisation decisions, product journey reviews, delivery progress, adoption performance, value realisation and key risks.
•    Ensure each AI use case has clear ownership across Product Owner, Business Owner, delivery team and governance stakeholders, with defined accountability for business outcomes, adoption, controls and value tracking.
•    Provide regular reporting to senior stakeholders and governance forums through AI portfolio dashboards, value dashboards, ROI tracking reports, adoption metrics and portfolio performance updates.

•    Support governance forums with evidence-based recommendations on prioritisation, investment, scaling, remediation, redesign or retirement of AI products.
•    Maintain disciplined portfolio controls so AI initiatives are traceable, measurable, outcome-led and aligned to business priorities, responsible AI expectations and enterprise governance standards.
•    Ensure product journey reviews, benefit cases, design decisions, user impacts, adoption evidence and value realisation outcomes are appropriately documented and available for management review.
•    Create a continuous improvement governance loop where feedback from Product Owners, Business Owners, users, delivery teams and governance stakeholders is used to refresh the roadmap, backlog, adoption approach and value strategy.
Key Deliverables
•    AI product roadmap & portfolio backlog (including generative & agentic AI initiatives) aligned to Risk & Compliance strategic priorities.
•    End-to-end process re-engineering blueprint co-created with product/business owners (leveraging generative & agentic AI) to transform key Risk & Compliance processes.
•    Central AI use case intake & prioritization framework ensuring value-driven selection and disciplined stage-gate development of AI solutions.
•    AI adoption strategy & integration plan (with clear adoption KPIs) to drive and measure widespread usage of new AI and data tools across Risk & Compliance teams.
•    Business enablement playbook & training modules to build R&C team proficiency and trust in advanced AI tools (e.g., generative & agentic AI solutions).
•    AI value measurement framework & dashboards tracking qualitative and quantitative benefits (productivity, risk reduction, ROI) delivered by deployed AI initiatives.
•    AI governance compliance documentation and requisite risk & compliance approvals for each AI product, evidencing Responsible AI and regulatory adherence.
•    Executive portfolio performance reports for R&C leadership highlighting AI-driven value creation, adoption progress, and future advanced AI opportunities. 

Regulatory & Business Conduct
•    Display exemplary conduct and live by the Group’s Values and Code of Conduct. 
•    Take personal responsibility for embedding the highest standards of ethics, including regulatory and business conduct, across Standard Chartered Bank. This includes understanding and ensuring compliance with, in letter and spirit, all applicable laws, regulations, guidelines and the Group Code of Conduct.
•      Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.
Key Stakeholders
•    AI Engineering                   Solution development
•    Data & Analytics    Data products and datasets
•    GenAI Specialists    Prompt design and context engineering
•    AI Assurance                   Model validation and governance
•    AI Operations                   Monitoring and lifecycle management
•    Business Units                   AI adoption and value realisation

Skills and Experience

•    Experience in product management, data analytics, or digital transformation
•    Strong knowledge of risk, compliance, or financial services operations
•    Ability to operate at the intersection of business, data, and technology
•    Strong stakeholder engagement and strategic thinking capabilities

About Standard Chartered

We're an international bank, nimble enough to act, big enough for impact. For more than 170 years, we've worked to make a positive difference for our clients, communities, and each other. We question the status quo, love a challenge and enjoy finding new opportunities to grow and do better than before. If you're looking for a career with purpose and you want to work for a bank making a difference, we want to hear from you. You can count on us to celebrate your unique talents and we can't wait to see the talents you can bring us.

Our purpose, to drive commerce and prosperity through our unique diversity, together with our brand promise, to be here for good are achieved by how we each live our valued behaviours. When you work with us, you'll see how we value difference and advocate inclusion.

Together we:

• Do the right thing and are assertive, challenge one another, and live with integrity, while putting the client at the heart of what we do
• Never settle, continuously striving to improve and innovate, keeping things simple and learning from doing well, and not so well
• Are better together, we can be ourselves, be inclusive, see more good in others, and work collectively to build for the long term

What we offer

In line with our Fair Pay Charter, we offer a competitive salary and benefits to support your mental, physical, financial and social wellbeing.

• Core bank funding for retirement savings, medical and life insurance, with flexible and voluntary benefits available in some locations.
• Time-off including annual leave, parental/maternity (20 weeks), sabbatical (12 months maximum) and volunteering leave (3 days), along with minimum global standards for annual and public holiday, which is combined to 30 days minimum.
• Flexible working options based around home and office locations, with flexible working patterns.
• Proactive wellbeing support through Unmind, a market-leading digital wellbeing platform, development courses for resilience and other human skills, global Employee Assistance Programme, sick leave, mental health first-aiders and all sorts of self-help toolkits
• A continuous learning culture to support your growth, with opportunities to reskill and upskill and access to physical, virtual and digital learning.
• Being part of an inclusive and values driven organisation, one that embraces and celebrates our unique diversity, across our teams, business functions and geographies - everyone feels respected and can realise their full potential.