Head, Advanced Analytics & Artificial Intelligence

Alliance Bank Malaysia Berhad; Kuala Lumpur; On-site; MYR 350,000 - 550,000; Full time

Job summary

Alliance Bank Malaysia Berhad is seeking a Head of Advanced Analytics and AI to build the Bank's data science and data engineering capabilities from the ground up. This hands-on leadership role combines data science leadership with data engineering ownership to establish the data platforms, governance, and MLOps required to operationalize AI at scale.

You will lead a junior team, define the target architecture for the analytics platform, and partner with Technology to deploy pipelines, feature

Qualifications

• Proven leadership of data science and analytics teams.
• Ability to define strategy, roadmaps and operating models for analytics.
• Experience building analytics platforms and governance for enterprise AI.

Responsibilities

• Define and execute the Data Science strategy, roadmap and operating model.
• Coach and upskill a team of Data Scientists.
• Lead development of predictive models, customer segmentation and AI use cases.
• Establish model lifecycle management including validation and monitoring.
• Promote best practices in ML, experimentation and responsible AI.
• Define target architecture for analytics and ML platform.
• Partner with Technology to deploy data pipelines, feature stores and deployment environments.
• Lead implementation of analytics technologies and MLOps capabilities.
• Ensure data assets are scalable, governed and production-ready.

Skills

Team leadership

Strategic planning

Stakeholder management

Tools

Python

SQL

Spark

Databricks

Snowflake

Microsoft Fabric

Azure

MLOps

CI/CD

Job description

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.
• 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:

• Python, SQL and Spark
• Data Engineering and Data Architecture
• Data mart and semantic layer design
• Databricks, Microsoft Fabric, Snowflake, Azure or equivalent cloud platforms
• MLOps, CI/CD, model deployment and monitoring