Top Machine Learning Jobs in Malaysia

The Director of Credit Risk Business Development will partner with clients to drive analytics and credit risk strategies, lead sales initiatives, and ensure successful analytics project implementations while achieving sales targets.

Lead tactical industrial engineering for NAND/SSD manufacturing to optimize operations, align supply and capacity, manage NPI-to-HVM material flow, install processes/automation, investigate material/logistics issues, provide management updates, and coach a team focused on data-driven decisions.

Lead product development for iPiD's Global Validate service, managing integrations to new countries/data providers, delivering API features with engineering and BD, analysing payment data and schemes, supporting clients and reports, and aligning cross-functional teams to launch and optimise product enhancements.

Lead engineering projects, automation and digitization, new product qualification, and machine/process reliability. Manage budgets, timelines, root-cause troubleshooting, maintenance strategy, and integration of advanced manufacturing technologies. Mentor and develop engineering team, ensure safety and compliance, and coordinate with regional/global stakeholders to drive productivity and operational excellence.

Lead AI transformation across Commercial: identify, prioritize, and deliver AI initiatives (forecasting, pricing, personalization, copilots, automation) that drive revenue, productivity, and data-driven decisions; translate business problems into scalable AI solutions; partner with Technology, Legal, Risk, and Data teams; build AI literacy and lead an execution-focused team.

Lead pre-sales and solution development for AI-driven customer engagements. Translate AI and ML capabilities into business value, build demos/POCs, support sales and architecture activities, engage C-level stakeholders, mentor technical teams, and collaborate with partners to create profitable, cross-practice solutions.

Lead roadmap, planning and delivery of ML/AI products for games. Scope ML efforts, track experiments and metrics, coordinate engineers and game teams, run Agile ceremonies, manage risks and stakeholders, and define evaluation plans for AI features.

Designs and applies statistical methods and predictive models to analyze manufacturing and business data, identify variation and patterns, create reports and dashboards, support cross-functional decision-making, and advance analytics infrastructure and data integrity.

Develop and deliver data analytics tools, algorithms, and methodologies to translate manufacturing data into insights for factory yield and process engineering. Apply predictive modeling, clustering, anomaly detection, time-series analysis, and ML frameworks to support engineers in semiconductor manufacturing.

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

Support development and deployment of custom AI/ML models for predictive maintenance, process multi-source engineering data for anomaly detection, and build automated data pipelines to feed live AI model health scores into Power BI dashboards.

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.

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.

Support the Polish Engineering team by assisting in development, programming, testing, and troubleshooting of manufacturing automation solutions; collect and analyze process data for improvements; document procedures; and research emerging automation and data science technologies.

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

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 SSD equipment roadmap and development, drive automation upgrades (SECS/GEM, MES, databases), improve UPH and yield, support APC/ML initiatives, collaborate with IT and automation teams, and manage new equipment budgets.

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.

Lead a small data analytics engineering team to build ELT pipelines, ensure supplier data integrity, implement automated data validation, and create real-time dashboards (e.g., Spotfire). Use statistical modeling to detect and resolve manufacturing variability, support continuous improvement in semiconductor fab/assembly/test, and provide shift-based analytics support and feedback for rapid countermeasures.