全职 Head, Advanced Analytics - Artificial Intelligence 工作, 薪水, Alliance Bank Malaysia Berhad Federal Territory 公司招聘中 - Ricebowl

Role Purpose

The Head of Advanced Analytics and AI will build the Bank's Data Science and Data Engineering capabilities from the ground up. This is a hands-on leadership role responsible for developing a junior team, establishing the data and machine learning platform, defining governance standards, and delivering advanced analytics, AI and decisioning capabilities that generate measurable business value.

The role combines Data Science leadership with Data Engineering ownership to ensure the Bank has the foundational infrastructure, data pipelines, feature stores, data marts and MLOps capabilities required to operationalize machine learning at scale.

Key Responsibilities:

a) Data Science Leadership

• Define and execute the Data Science strategy, roadmap and operating model.
• Coach, mentor and upskill a young team of Data Scientists.
• Lead development of predictive models, customer segmentation, propensity models, churn models, recommendation engines and AI use cases.
• Establish model lifecycle management including validation, monitoring, retraining and explainability.
• Promote best practices in machine learning, experimentation, responsible AI and analytics delivery.

b) Data Engineering & Platform Build

• Experience designing relational databases, data marts, semantic models and AI/analytics data structures that support machine learning, retrieval-augmented generation (RAG), knowledge management and enterprise AI solutions.
• Define the target architecture for the Bank's analytics and machine learning platform.
• Partner with Technology teams to establish data pipelines, data marts, feature engineering frameworks and model deployment environments.
• Lead implementation of modern analytics technologies such as Microsoft Fabric, Databricks, Snowflake, Azure ML or equivalent platforms.
• Establish MLOps capabilities, model monitoring, CI/CD processes and reusable machine learning assets.
• Ensure data assets are scalable, governed, production-ready and accessible for analytics and AI use cases.

c) Stakeholder Management & Transformation

• Partner with Business, Technology, Risk, Compliance and Data Governance stakeholders.
• Identify and prioritize use cases with measurable business impact.
• Translate business requirements into analytical and technical solutions.
• Drive adoption of data-driven decision making and AI capabilities across the organization.

Required Experience

• Minimum 12–15 years’ total experience across data science, advanced analytics, data engineering, machine learning, AI or analytics platform development.
• At least 5–7 years’ experience in a leadership role managing Data Science, Data Engineering, Advanced Analytics or AI teams.
• Minimum 3–5 years’ hands-on experience building analytics platforms, machine learning environments, data pipelines, data marts, feature engineering frameworks or MLOps capabilities.
• Experience operating in a low-to-medium data maturity environment, preferably within banking, financial services, fintech or another regulated industry.

Technical Expertise

Strong experience in:

• Machine Learning & AI (XGBoost, clustering, segmentation, NLP, GenAI)
• Python, SQL and Spark
• Data Engineering and Data Architecture
• ETL/ELT pipeline development
• Data mart and semantic layer design
• Databricks, Microsoft Fabric, Snowflake, Azure or equivalent cloud platforms
• MLOps, CI/CD, model deployment and monitoring