Job Description
Job Summary:
The purpose of this role is to lead the enterprise Data Science and Artificial Intelligence function for the Bank, reporting directly to the Head Data \& AI. This role is responsible for defining and executing the bank’s Data Science and AI strategy to deliver measurable business value, improve customer outcomes, strengthen risk management, and support responsible innovation across the organization.
The Head of Data Science / AI will partner with business, technology, risk, compliance, and operations teams to identify high-value use cases, develop advanced analytical and AI solutions, and industrialize models into scalable production capabilities. The incumbent is expected to combine strong technical depth in machine learning, statistical modelling, and AI with leadership capability in team building, stakeholder management, governance, and delivery execution. The role requires a strong understanding of model lifecycle management, data quality, regulatory expectations, and the practical application of AI within a banking environment.
This position will lead a team of data scientists, machine learning engineers, and analytics specialists to build predictive, prescriptive, and AI-driven solutions for business growth, customer engagement, fraud detection, risk optimization, operational efficiency, and decision intelligence. The incumbent must be able to translate complex analytical concepts into clear business outcomes and lead the organization in the adoption of modern, responsible, and scalable Data Science and AI practices.
Job Responsibilities:
- Lead the bank’s Data Science and AI strategy in alignment with enterprise priorities, ensuring initiatives are business-led, value-focused, and execution-ready.
- Work closely with business divisions to understand strategic priorities, identify data science and AI opportunities, and translate them into practical use cases, delivery roadmaps, and measurable business outcomes.
- Lead the design, development, validation, deployment, monitoring, and continuous improvement of machine learning, statistical, optimization, and AI models across banking domains.
- Use predictive and prescriptive modelling to improve customer experience, revenue growth, marketing effectiveness, portfolio performance, collections, fraud detection, service quality, and operational efficiency.
- Drive applied research and experimentation to evaluate emerging AI techniques, including generative AI, NLP, recommendation systems, forecasting, anomaly detection, and decision intelligence, where relevant to banking use cases.
- Establish and enforce standards for model development, model documentation, reproducibility, explainability, validation, performance monitoring, retraining, and retirement across the model lifecycle.
- Ensure all Data Science and AI solutions are developed and deployed in line with internal policies, data governance standards, information security controls, and applicable regulatory expectations.
- Partner with Risk, Compliance, Information Security, Legal, and Internal Audit teams to ensure responsible AI adoption, model risk governance, fair use, transparency, and proper control design.
- Build and maintain processes and tools to monitor model performance, data drift, concept drift, bias, accuracy, stability, and operational effectiveness in production environments.
- Oversee data preparation, feature engineering, experimentation frameworks, and training pipelines to improve the quality, scalability, and repeatability of advanced analytics and AI delivery.
- Collaborate with Data Engineering, Platform, and Architecture teams to industrialize models through robust MLOps practices, deployment pipelines, and integration into enterprise systems and channels.
- Develop analytical approaches to answer high-value business questions and provide fact-based recommendations to leadership for strategic and tactical decision-making.
- Mentor and lead data scientists, machine learning engineers, and analytics practitioners, building a high-performance team culture focused on innovation, delivery excellence, controls, and continuous learning.
- Educate business stakeholders on advanced analytics and AI best practices, model limitations, interpretation of outputs, and adoption considerations to improve business engagement and trust.
- Balance the demands of multiple projects simultaneously, managing delivery priorities, dependencies, risks, and resource allocation across the portfolio.
- Support regulatory inspections, internal audits, thematic reviews, and management reviews related to analytics, AI, models, data usage, and governance, and respond to observations in a timely manner.
- Identify and track value realization for Data Science and AI initiatives through outcome measurement, benefits tracking, and executive reporting.
**Minimum Qualifications
:**
- Minimum Master’s Degree in Data Science, Computer Science, Business Administration or Statistics. Preferably a Post Doctorate in the relevant field
**Minimum Experience
:**
- Minimum 10+ years of experience in Data Science, Advanced Analytics, Artificial Intelligence, or related quantitative domains, with at least 3–5 years in a leadership role managing teams and enterprise use cases.
- Proven experience in delivering data science and AI solutions from ideation to production in large and complex organizations.
- Experience in banking, financial services, or other regulated industries will be strongly preferred.
**Job Specific Skills
:**
- Strong problem-solving skills with an emphasis on business value creation, product development, and decision optimization.
- Strong hands-on and leadership-level experience using Python, SQL, and other analytical tools to manipulate data, engineer features, build models, and draw insights from large and complex datasets.
- Experience with a broad range of machine learning and AI techniques, including supervised and unsupervised learning, time-series forecasting, anomaly detection, NLP, deep learning, and optimization methods.
- Strong command of statistical techniques and concepts including regression, hypothesis testing, probability distributions, experiment design, model evaluation, and inferential analysis.
- Experience with modern ML and AI platforms, open-source frameworks, model deployment patterns, and MLOps practices.
- Strong understanding of model risk management, explainability, validation, bias controls, and performance monitoring in regulated environments.
- Experience with distributed data and computing technologies and modern data platforms for large-scale analytics workloads.
- Strong communication and storytelling skills, with the ability to explain complex analytical topics clearly to business and senior stakeholders.
- Ability to define strategy, govern execution, manage stakeholders, and lead cross-functional delivery teams effectively.
- Strong understanding of data governance, information security, privacy, and regulatory expectations relevant to AI and analytics in banking environments.
- A strong drive to learn, evaluate, and adopt new technologies and techniques responsibly.
Behavioral Skills:
- Strategic thinking and business acumen
- Leadership and people development
- Collaboration and influence
- Delivery focus and accountability
- Innovation with control awareness
- Sound judgment and decision-making