Principal Data Scientist

Bank Islam; Kuala Lumpur; On-site; MYR 150,000 - 250,000; Full time

Job summary

A leading financial institution in Kuala Lumpur seeks an experienced Principal Data Scientist to spearhead data science initiatives crucial for competitive advantage. This role integrates deep AI/ML expertise with business acumen to develop scalable, impactful solutions across various domains including banking and finance. Ideal candidates must possess a Master's or PhD in a quantitative field, with at least 10-15 years of relevant experience. Responsibilities include leading model development, mentoring teams, and ensuring compliance with governance standards.

Qualifications

• Minimum 10-15 years of extensive experience in IT analytics infrastructure and application design.
• Proficient in rapid prototyping on large datasets, aware of efficient algorithmic design.
• Experience in developing advanced models impacting business derived from various data types.

Responsibilities

• Lead scalable AI/ML model development with MLOps for deployment and improvement.
• Translate business challenges into structured data initiatives.
• Drive innovation with advanced AI and MLOps techniques.
• Mentor data scientists on MLOps practices.

Skills

AI/ML model development

MLOps practices

Data modeling

Business analytics

Performance monitoring

Education

Advanced degree (Masters or PhD) in a quantitative discipline

Job description

The Principal Data Scientist acts as a senior technical and strategic leader, driving high-value data science initiatives that shape the bank’s competitive advantage. This role combines deep expertise in AI/ML with strong business acumen to design, build, and deploy scalable, explainable, and impactful solutions across retail banking, corporate finance, trade services, and risk management. The Principal Data Scientist champions best practices in model development, operationalizes solutions through MLOps, and ensures adherence to regulatory, governance, and ethical standards. Additionally, the role provides mentorship to data science teams, fosters innovation through research and experimentation, and partners with business and technology stakeholders to translate complex analytical insights into actionable strategies and measurable business outcomes.

Duties and Responsibilities

• Lead scalable AI/ML model development with MLOps for deployment, monitoring, and continuous improvement.
• Translate business challenges into structured data initiatives using reproducible, traceable MLOps practices.
• Drive innovation with advanced AI, ML, and MLOps techniques like CI/CD and containerization.
• Ensure MLOps-compliant models with governance, ethical standards, versioning, monitoring, and regulatory compliance.
• Mentor data scientists in ML methods and MLOps practices, improving operationalization and technical excellence.
• Collaborate with engineering teams to optimize pipelines, orchestration, and feature stores for production data.
• Monitor and optimize models through automated pipelines, drift detection, retraining, and continuous performance tracking.
• Partner across departments embedding AI/ML workflows, supported by MLOps for efficient integration.
• Communicate analytical results and provide MLOps dashboards, ensuring clarity and influencing strategic decisions.
• Automate AI/ML lifecycle stages—training, validation, deployment, monitoring—using MLOps to streamline and scale operations.

Requirements

• Strong academic qualifications, with an advanced degree (Masters or PhD) in a quantitative discipline (typically information technology, computer systems, or mathematics) and advanced software certifications will be an added advantage.
• Minimum 10-15 years with:
  • Extensive experience in information technology analytics infrastructure, business systems analysis, business intelligence, application design, development, testing/software QA, implementation, coding, data modeling and reporting.
  • Broad based experience with rapid prototyping & production implementation on large datasets (terabytes/petabytes), being aware of efficient algorithmic design, memory and cpu usage/ scalability.
  • In-depth experience developing advanced models impacting business & derived from business analytics utilizing the landscape of structured, unstructured data, transactional data, text and speech analytics.