• Drive the design, development, and deployment of advanced analytics, machine learning and AI solutions that strengthen the bank’s risk management and regulatory compliance capabilities by transforming traditional, reactive risk management into proactive, predictive, and automated frameworks.
• The role sits within the AI CoE and partners with Risk, Compliance, Technology, and Data teams to:
  • Identify and prioritize high‑impact AI/ML use cases (e.g., financial crime, credit risk, operational risk, conduct & compliance monitoring).
  • Build robust, explainable models and analytical solutions that comply with internal policies and external regulations.
  • Embed models into business processes, ensuring they are trusted, monitored, and continuously improved.

Use Case Formulation & Solution Design

• Collaborate with Risk and Compliance stakeholders to translate business problems into clearly defined analytics/ML use cases with measurable outcomes.
• Conduct feasibility assessments, including data availability, model ability, risks, and expected value.
• Develop solution designs and model blueprints, including data pipelines, features, algorithms, controls, and monitoring approach.

Model Development & Implementation

• Design, build, and validate statistical and machine learning models for areas such as:
• Credit risk scoring and early warning indicators
• Financial crime (AML, fraud detection, sanctions screening enhancements)
• Conduct risk and compliance surveillance
• Operational and cyber risk analytics
• Implement models using appropriate tools and frameworks (e.g., Python, R, SQL, Spark) following the bank’s model risk and development standards.
• Ensure models are explainable, auditable, and compliant with model governance, regulatory expectations, and ethical AI principles.
• Partner with Technology/Data Engineering to productionize models and integrate them into business workflows and platforms.

Data Management & Feature Engineering

• Source, explore, and assess the quality of internal and external datasets relevant to risk and compliance.
• Design and implement robust data preprocessing, feature engineering, and variable selection pipelines.
• Ensure data usage adheres to data privacy, security, and governance standards.

Model Validation, Monitoring & Governance

• Define, implement, and document model performance metrics and validation approaches (e.g., accuracy, stability, fairness, drift).
• Support internal model validation, audit, and regulatory reviews by providing clear documentation and technical explanations.
• Design monitoring dashboards and processes to track model performance, drift, and usage; proactively recommend recalibration or redevelopment where needed.
• Ensure adherence to model risk policies, governance frameworks, and regulatory guidelines for AI and advanced analytics.

Stakeholder Engagement & Change Management

• Present analytical insights and model outcomes in a clear, non‑technical manner to senior stakeholders in Risk, Compliance, and Business.
• Collaborate with Operations, Technology, and Change teams to embed models into processes, controls, and decisioning systems.
• Provide training and guidance to Risk and Compliance teams on the use and limitations of analytical models and AI tools.

• 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

Other Responsibilities

• Exposure to advanced machine learning methodologies is a plus
• Embed “Here for Good” and Group values within the team and across partner interactions.
• Perform other responsibilities as assigned under Group, country, business, or functional policies and procedures, consistent with role scope.

• GenAI/agentic concepts
• Product & portfolio management (intake, prioritisation, lifecycle, adoption)
• AI risk management literacy (validation, drift/monitoring concepts)
• Stakeholder management and operating model design

Education & Qualifications

• Degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Engineering, or related discipline.
• Postgraduate degree and/or relevant professional qualifications in analytics, risk, or compliance are an advantage.

Experience

• Experience designing and delivering digital, data, or AI solutions in a complex, regulated environment (financial services or consulting preferred).
• Hands‑on exposure to GenAI/LLM projects (POCs, pilots, or production solutions).
• Background in one or more of: solution architecture, data/ML engineering, or advanced analytics.

Technical Skills

• Strong hands‑on experience with advanced analytics and ML techniques, such as:
• Supervised and unsupervised learning (classification, regression, clustering, anomaly detection)
• Time series modelling and forecasting
• Natural Language Processing (for compliance text analysis, document review, surveillance)
• Proficiency in programming languages and tools commonly used for data science (e.g., Python, R, SQL; experience with Spark or similar big data tools is a plus).
• Experience in model lifecycle management: development, validation, deployment, and monitoring.
• Familiarity with MLOps concepts and tools (e.g., model versioning, CI/CD for ML, monitoring platforms) is desirable.
• Solid understanding of data management practices, including data quality, lineage, and governance.

Risk & Compliance Knowledge

• Practical experience in risk, financial crime, or compliance analytics within financial services or consulting is highly desirable.
• Understanding of regulatory expectations related to model risk management, use of AI, and data privacy within banking.
• Awareness of challenges such as bias, fairness, explainability, and ethical considerations in AI for risk and compliance.

Analytical & Problem-Solving Capabilities

• Strong analytical thinking with the ability to decompose complex, ambiguous problems into structured analytical tasks.
• Ability to balance technical sophistication with practical constraints (data quality, regulatory requirements, operational reality).
• Skilled at quantifying benefit/risk trade‑offs and recommending pragmatic solutions.

Stakeholder & Communication Skills

• Ability to communicate complex technical concepts in clear business language to non‑technical stakeholders.
• Strong stakeholder management and influencing skills; able to collaborate effectively across Risk, Compliance, Technology, and Business.
• Experience working in cross‑functional, agile or project‑based teams.

Personal Attributes

• High integrity and strong risk and control mindset.
• Curiosity and proactive learning attitude towards emerging AI, analytics, and regulatory trends.
• Outcome‑oriented, with a focus on delivering value safely and compliantly.
• Resilient, adaptable, and comfortable working in a fast‑evolving environment.

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

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.