AEON Credit Service (M) Berhad Hiring! Full Time Head of Data Science in Federal Territory - Ricebowl

Main Purpose of Job

The Head of Data Science is responsible for setting the vision, strategy, and execution of the organization’s data science and analytics engineering capabilities. This role leads multidisciplinary teams comprising Data Scientists and Analytics Engineers, ensuring that data products, models, machine learning and analytical insights are scalable, trusted, and deliver measurable business impact.

This leader acts as a strategic partner to business and technology stakeholders, bridging data engineering, advanced analytics, predictive modelling and decision‑making across the enterprise.

Principle Responsibilities & duties

1. Data Science & Analytics Strategy: Works with Chief Data Officer to establish clear roadmaps covering analytics engineering, machine learning, and insight generation.
2. Analytics Engineering Leadership: Oversee analytics engineering practices, including semantic layer design, data models, metrics definitions, and reusable datasets. Ensure high-quality, scalable, and well-governed analytical datasets for downstream consumption
3. Advanced Analytics & ML Delivery: Lead the development and deployment of predictive, prescriptive, and descriptive analytics solutions. Ensure data science methodologies are robust, explainable, and production-ready
4. End-to-End ML Ownership: Own analytics and data science solutions from problem definition to production deployment and value realization. Partner with AI/ML engineering and IT teams to operationalize models and analytics products
5. Stakeholder & Business Partnership: Act as a trusted advisor to senior leadership on data-driven decision making. Translate complex analytical outputs into business-relevant insights and recommendations
6. Data Governance, Quality & Risk Oversight: Set standards for data quality, documentation, lineage, model governance, and reproducibility. Ensure compliance with data privacy, security, and regulatory requirements
7. People Leadership & Capability Building: Lead, mentor, and grow teams of Data Scientists and Analytics Engineers. Define career pathways, skills frameworks, and performance expectations
8. Innovation & Thought Leadership: Identify opportunities to adopt emerging techniques in AI, ML, and analytics engineering. Stay current with industry trends and assess applicability to business needs

Qualifications

• Bachelor’s degree, Master’s Degree or PhD in Data Science, Computer Science, Statistics, Engineering, or related field
• 10+ years of experience in data, analytics, or AI roles, with significant leadership responsibilities
• Proven experience leading both analytics engineering and data science teams
• Strong understanding of data modeling, analytics-layer design, and machine learning techniques
• Experience translating business strategy into scalable data solutions
• Experience in cloud-based data platforms (e.g., Azure, AWS, GCP)
• Familiarity with modern analytics stacks (e.g., dbt, BI platforms, feature stores)
• Experience establishing or scaling data science team is a plus
• Industry experience in financial services is a plus