Job Summary
The Director, Advanced Analytics & AI is a techno-functional leader responsible for designing, building, and industrialising advanced analytics and machine learning solutions that enhance the bank’s financial risk management, regulatory compliance, and decision-making capabilities.
The role sits at the intersection of:
- Business (Risk / Compliance)
- CDO (data products)
- Technology (engineering & product ionisation)
and ensures an end-to-end lifecycle from use case discovery to production-grade deployment, aligned to regulatory and model governance expectations
Key Responsibilities
Strategy
- Lead identification, prioritisation, and shaping of high-impact analytics & ML use cases across Financial Risk and Compliance domains
- Translate regulatory and business requirements into analytical problem statements and solution blueprints
- Own business value realisation (efficiency, risk reduction, control effectiveness, insights)
- Aligns with CoE mandate to drive value-led, outcome-focused AI delivery.
Business
- Define end-to-end solution architecture for analytics and ML use cases:
- Feature engineering, model selection, evaluation strategy
- Data sourcing and transformation requirements
- Establish and enforce design patterns, reusable components, and modelling standards
- Reflects role of lead architect + capability owner in CoE model
Processes
- Personally lead or closely supervise the development of POCs and prototypes for:
- New analytical patterns
- Complex or regulatory-sensitive use cases
- Validate:
- feasibility
- performance
- explainability
- Core expectation: prototype → validate → scale recommendation
- Establish reusable:
- feature engineering pipelines
- model templates
- evaluation frameworks
- Drive scaling from POCs to enterprise-grade solutions
- Critical to avoid “one-off analytics” and move to repeatable AI products
- Define requirements for AI-ready data products with CDO teams:
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- curated datasets
- feature stores
- data quality & lineage
-
- Ensure alignment between:
-
- data supply (CDO)
- analytics consumption (AI CoE)
-
- Aligns with CoE positioning as bridge between data and intelligence
People & Talent
- Lead through example and demonstrate the bank’s culture and values
Key Responsibilities
Risk Management
- Work with Technology and Data Engineering teams to industrialise solutions into production
- Provide oversight for:
-
- model integration
- pipelines, APIs, and deployment frameworks
-
- Ensure:
-
- functional correctness
- alignment to business intent
-
- Consistent with model:
- Risk/AI CoE → owns logic, validation
- Technology → owns runtime & engineering
- Embed analytics into:
-
- credit risk models
- stress testing & forecasting
- financial crime detection
- regulatory reporting analytics
-
- Ensure outputs are:
-
- explainable
- auditable
- regulator-ready
-
Governance
- Define and enforce end-to-end model lifecycle controls:
-
- model documentation and explainability
- validation frameworks
- monitoring (drift, bias, performance)
-
- Ensure compliance with:
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- Model Risk Management
- AI governance, fairness, explainability
- Regulatory expectations on AI usage
-
- Strong emphasis on governed lifecycle and audit-readiness
Reporting & Stakeholder Communication
- Act as primary interface between business stakeholders, CDO, and Technology
- Engage senior stakeholders to:
- align priorities
- drive adoption
- manage regulatory expectations
- Role explicitly requires strong business-tech bridging capability
Team Leadership & Capability Building
- Lead multidisciplinary teams of:
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- data scientists
- ML engineers
- analytics specialists
-
- Coach teams on:
-
- model development best practices
- regulatory constraints
- production readiness
-
- Build reusable:
-
- frameworks
- accelerators
- experimentation standards
-
Key Stakeholders
- Data & Analytics
- GenAI Specialists
- AI Operations
- Business Units
- Technology (AI Engineering Lead; Data Engineering Lead; platform owners)
- Functions CDO stakeholders (standards, platform, data foundations)
- AI Services
- Legal, Privacy, Cyber Security, Model Risk, Operational Risk
- Internal Audit / Assurance partners
- COO / Finance partners (capacity and investment planning)
- AI Solutions Team
- Compliance & Governance
Skills and Experience
Technical and Operational Skills
- Strong Hands-On Experience In:
-
- Machine Learning (Classification, Regression, Clustering, Anomaly Detection)
- Time Series Modelling And Forecasting
- Nlp / Text Analytics (For Compliance And Surveillance Use Cases)
-
- Proficiency In:
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- Python / R / Sql
- Big Data Frameworks (E.G., Spark)
-
- Deep Understanding Of:
-
- Model Lifecycle (Development → Validation → Deployment → Monitoring)
- Mlops (Ci/Cd, Versioning, Monitoring)
- Data Management (Quality, Lineage, Governance)
-
- Strong experience in:
-
- Financial Risk (credit risk, stress testing, exposure analytics)
- Regulatory compliance / financial crime analytics
-
- Solid understanding of:
-
- Model Risk Management frameworks
- Regulatory expectations on AI, models, and data
-
Role Specific Technical Competencies
- GenAI/agentic concepts
- Product & portfolio management (intake, prioritisation, lifecycle, adoption)
- AI risk management literacy (validation, drift/monitoring concepts)
- Stakeholder management and operating model design
- GenAI/agentic concepts
- Product & portfolio management (intake, prioritisation, lifecycle, adoption)
Leadership & Delivery Experience
- Proven track record of:
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- delivering analytics solutions from POC to production
- leading cross-functional teams across business, data, and technology
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- Experience operating in matrixed environments (business + CDO + tech)
AI Governance & Responsible AI
- Strong understanding of:
-
- explainability
- bias/fairness
- ethical AI
- audit and control requirements
-
Problem Solving & Strategic Thinking
- Ability to:
-
- break down complex business problems into analytical solutions
- balance technical sophistication with regulatory and operational constraints
-
Qualifications
- Degree in Data Science, Statistics, Mathematics, Engineering, Computer Science or related field
- Postgraduate (Masters/PhD) in quantitative discipline preferred
- Extensive programming experience using SQL, Python, SAS, Excel Automation.
- Strong analytical mindset with excellent analytical, logical, reasoning and problem-solving skills.
- Excellent written and oral communication skills at all levels (i.e. colleagues to senior management) and situations (i.e. one-on-one to presentations)
- Exposure to advanced machine learning methodologies is a plus
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.