Top Machine Learning Jobs in Malaysia

Lead manufacturing quality and reliability activities including excursion management, NPI readiness, process validation, SPC and statistical analysis, predictive reliability modeling, root-cause investigations, corrective actions, and cross-functional problem-solving to ensure manufacturing excellence and continuous improvement.

Ensure manufacturing quality and reliability through excursion management, risk assessments, data-driven analysis (including ML), process improvement, floor audits, reliability testing, technical reporting (8D), and cross-functional collaboration to support high-volume production.

Perform AI/ML development to integrate machine learning into product/process R&D for lubricants, emulsifiers, or silicones. Preprocess and clean multi-source experimental datasets and technical documentation. Train and optimize Bayesian/Random Forest models using sequential active learning; validate performance and translate outputs into actionable R&D insights. Collaborate with R&D, application teams, and technology partners for integration.

The IT Consultant will design finance processes, lead automation initiatives, and manage SAP FI/CO solutions while ensuring project readiness and ongoing support.

The Data Annotator is responsible for labeling datasets, reviewing annotations for quality, collaborating with AI teams, and meeting annotation targets.

Lead the core verification engine for global address and contact verification: drive discovery, define product strategy, partner with engineering and data science to improve accuracy, latency, throughput and resilience, productise AI/ML components responsibly, engage enterprise customers, run experiments and measure outcomes (conversion, match rates, cost), and deliver platform capabilities (APIs/SDKs, observability, SLAs) at scale.

Develop, review and monitor credit rating models for the wholesale banking portfolio. Work with large datasets, perform model development, testing, documentation, UAT, and regulatory submissions. Produce performance monitoring reports, engage business experts for insights, and improve model monitoring and automation.

As a Data Scientist, you will develop analytical models, manage data infrastructure, translate data into insights, and collaborate on projects across teams.

Support day-to-day business activities by gathering user requirements, analyzing data, creating documentation, and assisting in AI-related projects for a smoother communication between business and development teams.

Perform portfolio analytics and scorecard model development for capital and provisioning. Conduct Basel EL and RWA analysis, regulatory stress testing, forecasting, reporting, dashboard development and automation. Support systemisation, UAT/regression testing, stakeholder engagement and continuous process improvements using Python and visualization tools.

The Process Engineer manages process engineering activities for manufacturing, focusing on cost, quality, and delivery improvements, troubleshooting, and implementing process control methodologies.

As a Lead Data Scientist, you'll manage end-to-end projects, build and deploy AI models, analyze data, and collaborate with teams to enhance user experience.

The Data Analyst role involves annotating emails, identifying malicious URLs, labeling data for AI training, ensuring process adherence, and supporting project management KPIs.

The Data Scientist role involves analyzing data, collaborating with teams on AI/ML projects, and developing models to improve product development and efficiency.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead and manage the Group's non-financial and operational risk framework, oversee RCSA and PRISM ORM data quality for Head Office and LBUs, act as risk steward, review incidents and risk appetite breaches, report to governance forums, provide training and ad-hoc support, and lead risk transformation projects to improve risk management and controls.

The Sales Director, APAC will lead sales efforts in the Aviation sector, manage accounts, generate new opportunities, and strengthen customer relationships to drive growth.

This Data Analyst role involves annotating and examining emails, identifying malicious links, labeling data for AI, and ensuring compliance with processes. The candidate will also monitor KPIs and assist project management.

Lead and expand a major financial-institution account by building C-suite relationships, designing enterprise SaaS solutions, negotiating multi-million-dollar deals, driving account growth, and influencing product strategy. Travel up to 50% and collaborate across sales, product, delivery, and marketing.

Lead commercialization and solution delivery of Tencent Cloud Risk Control Solution (TCRS) in overseas markets. Design anti-fraud, business security, and financial risk solutions across registration, login, transactions, payments, and credit lifecycles. Support end-to-end sales from discovery to POC and contract, translate client requirements into product roadmaps, enable sales with training/materials, and coordinate cross-functional teams to deliver scalable industry solutions.

The Technical Consultant will drive process improvements using AI, support strategic client implementations, and innovate payroll solutions through technology.

The Senior AI Architect will design and implement scalable AI solutions across APAC, mentor teams, and drive AI best practices while engaging with customers.

Design, integrate, and manage the marketing technology stack (CRM, automation, AI agents) to capture intent signals, automate lead scoring and segmentation, optimize CRM strategy, and support demand generation through cross-functional collaboration.

Work within the WB Portfolio Management Unit to develop scorecard models, perform portfolio analytics (Basel EL/RWA), forecasting, regulatory stress testing, reporting, dashboard development and automation, stakeholder engagement, UAT/regression testing, and support systemisation initiatives using Python and visualization tools.