Top Machine Learning Jobs in Malaysia

Monitor and improve semiconductor manufacturing equipment performance, uptime, utilization, reliability, yield, and productivity. Troubleshoot failures, lead root cause analysis, implement corrective and preventive actions, and support maintenance programs. Lead equipment installation, commissioning, and qualification. Analyze manufacturing data and apply AI, machine learning, automation, and digital tools to improve operations. Ensure safety and environmental compliance, train technicians, and collaborate across engineering, production, quality, and maintenance teams.

Develops and manages electrical facility systems, including high- and low-voltage equipment, power distribution, maintenance, relay calibration, and regulatory compliance. Leads smart facilities initiatives using AI, machine learning, predictive maintenance, analytics, digital twins, dashboards, and automated diagnostics to improve reliability, energy efficiency, capacity planning, and operational performance. Collaborates with IT, automation, data science, and global facilities teams while supporting troubleshooting, reporting, and facility expansion.

Direct operation, reliability, maintenance, capacity planning, and lifecycle management for site-wide mechanical, electrical, and energy systems in a semiconductor manufacturing environment. Leads root cause analysis, predictive maintenance, digital twin, IIoT, AI, automation, energy optimization, and smart-facility initiatives. Ensures regulatory and ISO compliance, resolves complex operational issues, collaborates cross-functionally, and mentors engineers and technicians.

Maintain and support ELV and critical facility systems, including UPS, access control, networks, solar monitoring, VFDs, PQMS, FMCS, BMS, and SCADA. Lead AI, machine learning, IIoT, predictive maintenance, dashboards, digital twins, intelligent alarming, and smart-facility initiatives. Manage contractors, facility upgrades, CAPEX projects, troubleshooting, root-cause analysis, documentation, and EHS compliance while collaborating with Automation, IT, Cybersecurity, and Facilities teams.

Develop, optimize, and maintain SSD test programs for high-volume manufacturing and NPI. Automate testing with scripts, enhance coverage, debug failures, collaborate with product/equipment teams, monitor metrics, manage test transfers, and mentor junior engineers.

Own the vision, strategy, and roadmap for a shared lending platform supporting multiple products and markets. Lead platform architecture decisions, real-time credit decisioning, API and configuration design, vendor evaluations, regulatory reporting, data aggregation, and ML-assisted credit capabilities. Translate risk and compliance needs into engineering-ready specifications while coordinating Credit, Risk, Engineering, Design, and country teams. Manage dependencies, delivery sequencing, and the full product lifecycle from research through launch and ongoing iteration.

Leads enterprise presales and consultative selling for Experian’s Ascend Platform across Malaysia. Develops the presales practice, originates and structures multimillion-dollar SaaS deals, engages C-suite banking and lending stakeholders, and architects end-to-end credit risk and decisioning solutions. Responsibilities include technical demonstrations, workshops, proposals, architecture blueprints, RFP responses, business process assessments, outbound campaigns, and collaboration on data science, GenAI, ModelOps, and real-time decisioning solutions.

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

Develop a plant reliability threat dashboard using PI data, SAP, and other sources. Improve a Microsoft Azure machine learning model for failure data classification and gain exposure to OutSystems digital programs.

Analyze and interpret complex data using machine learning, artificial intelligence, and advanced analytics. Transform findings into actionable insights that support organizational decision-making, business strategy, and improved outcomes. Clearly communicate technical concepts and data insights to both technical and non-technical audiences.

Support APAC Claims Operations by producing and automating reports, reconciling multi-system data, resolving quality issues, and developing scalable dashboards. Build SQL queries, Python automation workflows, data validation processes, and documentation. Deliver ad hoc analysis and leadership insights while piloting AI applications such as claims classification, anomaly detection, document summarization, and natural-language querying. Partner across markets to improve reporting efficiency, data quality, and operational decision-making.

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.

Develop and improve data science and machine learning models for marketing and consumer insights, including attribution, personalization, forecasting, experimentation, and marketing measurement. Prepare datasets, define evaluation metrics, support model deployment, and generate insights from media, pricing, promotions, and syndicated data. Collaborate with product owners, data engineers, and machine learning engineers; contribute to responsible AI governance, code reviews, reusable AI assets, and mentorship of junior team members.

Leads APAC Data and AI enablement by aligning regional business priorities with enterprise technology strategy. Translates business problems into scalable use cases, roadmaps, and measurable outcomes; partners with platform, data science, engineering, risk, compliance, and operations teams; drives adoption and value realization; and builds responsible AI capabilities through training, governance, champion networks, and reusable practices.

Leads finance transformation by owning core financial processes, modernizing ERP and planning systems, deploying AI and automation, partnering with engineering and data teams, redesigning workflows, establishing controls, and driving organizational adoption. The role requires senior finance leadership, strong systems expertise, hands-on AI transformation experience, process governance, and the ability to translate between finance and technology.

Leads AI- and data-driven manufacturing transformation strategies for Life Sciences clients. Advises C-suite and operations leaders, develops AI strategies, business cases, roadmaps, operating models, and responsible AI plans, and oversees large-scale greenfield and brownfield manufacturing programs. Drives digital twin, automation, data platform, advanced analytics, generative AI, and AI/ML deployments from proof of concept through enterprise scale. Builds executive relationships, originates opportunities, manages partner ecosystems, develops manufacturing capabilities, and leads teams, delivery, financial performance, and risk.

Provides Japanese-language technical customer support for Zebra products, independently troubleshooting moderately complex hardware, software, and cloud issues. Manages support tickets and service-level agreements, collaborates with cross-functional teams, improves support processes, contributes product insights, and develops training for entry-level staff. The role requires analytical problem-solving, multitasking, prioritization, and some experience with AI-driven support tools.

Drive enterprise AI solution sales across the APAC region by identifying target accounts, building qualified pipelines, leading consultative discovery, and closing complex solution deals. Translate AI capabilities into measurable business outcomes for technical and executive stakeholders, negotiate contracts, develop partnerships with cloud and technology providers, and collaborate with architects, marketing, and product teams. Success is measured through pipeline generation, won deals, pilot-to-production conversion, and deal-cycle performance.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.

Leads enterprise-wide business analysis and digital transformation initiatives across Operations. Responsibilities include gathering requirements, mapping processes, developing business cases, redesigning customer experiences, identifying AI and automation opportunities, managing transformation roadmaps, influencing senior stakeholders, supporting solution delivery, and tracking governance, performance, risks, and benefits realization.

Serves as the Enhanced Due Diligence subject matter expert, improving research content, methodology, risk scoring, sanctions screening, report writing, templates, and training. Supports complex client requirements, pre-sales activities, issue resolution, product testing, knowledge management, and database maintenance. Delivers training and workshops, partners with technology and product teams, and may supervise junior staff. Requires expertise in KYC, AML, ABAC, third-party risk, global due diligence regulations, analytical writing, and Microsoft Office.

Research AI, machine learning, robotics, autonomous vehicles, and data annotation topics, then create technical blogs, case studies, explainers, whitepapers, and thought leadership content. Translate complex concepts into clear, commercially relevant narratives for enterprise audiences. Collaborate with technical, Growth, and Marketing teams, incorporate editorial feedback, optimize content for readability and SEO, source supporting research, and maintain consistent messaging across digital channels.

The Sales Manager will drive customer acquisitions and revenue growth, manage complex sales cycles, and present Cloud solutions at executive levels while maintaining long-term relationships with clients.