Top Machine Learning Jobs in Malaysia

Develop machine learning and advanced analytics solutions for risk management, including predictive models, GenAI, agentic AI, data pipelines, dashboards, and automated reporting. Analyze complex datasets to identify emerging risks, control weaknesses, and non-compliance. Collaborate with stakeholders to deliver scalable analytical solutions, support model deployment and monitoring, and communicate insights to technical and executive audiences.

Lead the global strategy and roadmap for recommendations and personalization across three ecommerce platforms. Partner with data science, analytics, engineering, and regional teams to translate customer intent and machine-learning capabilities into scalable discovery experiences. Own experimentation, relevance, engagement, conversion, and commercial outcomes while balancing shared capabilities with local market needs.

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.

Resolves moderately complex technical customer issues across Zebra products, manages support tickets and SLA adherence, collaborates with cross-functional teams, improves support processes, develops training for junior staff, and contributes to technical reviews and product improvements. The role requires knowledge of MDM platforms, SQL, cloud services, Linux, Android, hardware, software, and cloud-based systems, with rotating 24x7 shift coverage.

Lead risk-based internal audit planning and end-to-end engagements for a global cryptocurrency exchange. Evaluate governance, risk, and controls; produce audit reports for management and the Board; monitor remediation; and use SQL, AI, and advanced analytics to improve audit testing and risk assessment. Collaborate with Compliance, Risk, and Finance teams while addressing cross-border regulatory requirements and emerging risks in cryptocurrency, DeFi, and blockchain.

Develop and maintain machine learning models and AI applications, including data preprocessing, feature engineering, training, debugging, deployment, and production monitoring. Collaborate with cross-functional stakeholders to translate business needs into scalable AI/ML solutions. Apply cloud development, containerization, APIs, microservices, and LLM techniques such as tokenization, RAG, and reinforcement learning. Participate in code reviews, follow coding standards, and continuously expand technical expertise.

Develop and maintain machine learning models and AI applications, including data preprocessing, feature engineering, model training, debugging, and production deployment. Collaborate with cross-functional stakeholders to translate business needs into scalable AI/ML solutions. Apply LLM techniques such as tokenization, RAG, and reinforcement learning, while following coding standards, participating in code reviews, and continuously improving technical expertise.

Drive new-logo acquisition and expansion across Napier AI’s Asia-Pacific client base. Build territory plans, develop senior relationships within financial institutions and regulated organizations, manage complex enterprise sales cycles, deliver proposals and business cases, navigate procurement and legal processes, negotiate commercial terms, and partner with internal teams on upsell, cross-sell, renewals, and customer growth.

Leads end-to-end Record-to-Report process architecture across 16 European and APAC markets. Owns standardization, IFRS-compliant continuous close, SAP S/4HANA optimization, automation, AI adoption, internal controls, and SSC governance. Manages and develops the regional R2R team while partnering with IT, accounting, tax, audit, and finance leadership to deliver large-scale transformation and improved reporting accuracy.

Leads enterprise AI transformation across banking business units, identifying use cases, developing strategies, and overseeing AI projects from proof of concept through deployment and monitoring. Collaborates with business, data science, engineering, and IT teams; evaluates technologies and vendors; establishes responsible AI governance; mentors specialists; and presents business cases and recommendations to senior management.

Build, deploy, and maintain scalable machine learning systems; productionize data science prototypes; manage end-to-end ML workflows using MLOps across cloud and on-premises environments. Collaborate with data scientists, engineers, and product teams, improve engineering practices, mentor colleagues, and contribute to technical roadmaps. The role requires strong Python, database, containerization, statistical modeling, and production software development expertise.

Leads group-wide non-financial and operational risk management across methodology, risk appetite, controls, RCSAs, incidents, governance reporting, and risk training. Oversees operational risk data quality, provides independent challenge, investigates root causes, tracks remediation, and supports strategic risk transformation projects. Requires strong financial services knowledge, stakeholder influence, data analytics, AI, machine learning, automation, communication, and change leadership capabilities.

Conduct surveillance reviews and test business controls across conduct, financial crime, and operational risk. Analyze complex datasets to identify emerging risks and control weaknesses, develop automated solutions, support investigations and regulatory reviews, track remediation actions, escalate material findings, and improve risk oversight. The role also communicates technical findings to stakeholders and leads cross-functional initiatives.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Leads global supply chain optimization by designing scalable processes, standards, training, metrics, and technology capabilities. Oversees procurement and ERP roadmaps, Oracle Fusion governance, AI and analytics adoption, MRP policies, standard costs, supplier performance, and continuous improvement. Drives transformation initiatives, change management, shared services, executive reporting, and M&A integration across divisions and geographies.

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.

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.

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.

Leads complex data mining and machine learning initiatives, develops data-driven insights and business solutions, tracks analytics revenue, and aligns stakeholders on deliverables and standards. Provides thought leadership on emerging data science trends, mentors data scientists and team members, supports analytics projects, and contributes to data science strategy and best practices.

Develops and supports dispatching and full-automation software solutions for semiconductor manufacturing. Responsibilities include solution analysis, design, coding, code reviews, documentation, integration, testing, product assurance, second-line automation support, customer requirement analysis, and global deployment assistance. The role troubleshoots complex factory systems, supports onsite customer rollouts, uses SQL and scripting for data manipulation, and provides informal guidance to new team members.

Develops data-driven insights and machine learning models to support business decisions and revenue or cost goals. Provides analytics reporting, collaborates with business stakeholders to align solutions and standards, contributes to data science strategy, and mentors team members on analytics projects.

Develops end-to-end fund security and anti-fraud risk strategies across financial products. Analyzes transaction data with Hive SQL and Python, reconstructs attack chains, quantifies losses, evaluates experiments, and deploys real-time rules. Responds to major security incidents, builds risk intelligence libraries, monitors metrics, and improves decision systems through collaboration with algorithm, product, engineering, compliance, and operations teams.

Resolves moderately complex technical customer issues across multiple Zebra products, manages support tickets within SLA targets, collaborates with cross-functional teams, improves support processes, develops training for entry-level staff, and contributes to technical reviews and product improvement insights.

Ensures product and process quality and reliability during manufacturing ramp-up to high-volume production. Responsibilities include developing inspection and testing mechanisms, conducting audits and quality assessments, monitoring performance indicators, managing excursions, performing risk assessments, dispositioning discrepant materials, implementing early detection systems, driving continuous improvement, and addressing customer-reported manufacturing concerns through data-driven engineering solutions.