Senior Data Analyst - Manufacturing

ResMed (Malaysia) Sdn Bhd (MY); Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

ResMed (Malaysia) Sdn Bhd (MY) is seeking a Senior Manufacturing Data Analyst to analyse manufacturing data and develop analytics solutions supporting operational and financial outcomes across value streams.

You will work with manufacturing leaders, engineers and data specialists to translate problems into analytics, dashboards and decision-support tools, spanning machine data, quality, downtime and material flow. Strong communication of findings is essential.

Qualifications

• Bachelor’s degree in Engineering, Manufacturing, Supply Chain Management, Computer Science, Data Science, Information Systems, Business or related field; advanced degree preferred.
• 6+ years delivering analytics, automation or AI-enabled solutions in manufacturing or supply chain environments.
• Strong understanding of manufacturing processes, value streams and improvement opportunities.
• Technical depth to assess feasibility, data readiness, systems integration and risk; able to work with technical specialists.
• Experience with manufacturing data and technology environments such as ERP, MES, equipment or IoT data.

Responsibilities

• Define manufacturing problems, analytical questions, hypotheses, measures of success and data requirements with value stream owners and engineers.
• Analyse production, process, quality, downtime, cycle-time, labour, material-flow and cost data to identify loss drivers and improvement opportunities.
• Enable near-real-time manufacturing performance visibility, including part-level data and OEE diagnostics.
• Apply data profiling, descriptive statistics, correlation, segmentation and time-series analysis.
• Translate analyses into practical operational insights, recommendations and prioritised actions.
• Develop, test and maintain dashboards, reports and models embedded in workflows.
• Create clear metric definitions, calculation logic and data-quality checks.
• Collaborate with data engineers and OT/IT teams to define data pipelines.
• Support user acceptance testing, release readiness, training and adoption.

Skills

Analytical thinking

Communication

Cross-functional collaboration

Project management

Education

Bachelor's degree in Engineering or related field

Advanced degree preferred

Tools

ERP systems

MES

IoT data platforms

Job description

Senior Manufacturing Data Analyst

The Senior Manufacturing Data Analyst is a key delivery role within ResMed’s Manufacturing Excellence Program, responsible for analysing manufacturing data and developing practical analytics solutions that support measurable operational and financial outcomes across manufacturing value streams.

The role works closely with manufacturing leaders, engineers, technicians, operators, data and technology specialists to translate operational problems into clear analytical questions, data requirements, insights, dashboards and decision-support tools. It supports solutions from discovery and data validation through development, deployment, adoption and ongoing improvement.

Success requires strong manufacturing and continuous-improvement knowledge, hands‑on analytical capability and the ability to communicate findings clearly to both operational and technical audiences. The role will work across equipment, process, quality, labour and material‑flow data as well as supporting predictive and prescriptive analytics in partnership with data science and engineering specialists.

Responsibilities

Manufacturing Data Products and Insights

• Partner with value stream owners, site teams, engineers and frontline users to define manufacturing problems, analytical questions, hypotheses, measures of success and data requirements.
• Analyse production, process, quality, downtime, cycle‑time, labour, material‑flow and cost data to identify loss drivers, trends, constraints, process drift and improvement opportunities.
• Enable near‑real‑time manufacturing performance visibility, including part‑level production data, OEE and performance, availability and quality loss diagnostics.
• Apply appropriate analytical methods including data profiling, descriptive statistics, correlation, segmentation, root‑cause analysis, statistical process control and time‑series analysis.
• Translate complex analysis into practical operational insights, clear recommendations and prioritised actions.
• Develop, test and maintain dashboards, reports, analytical models and decision‑support tools that are reliable, user‑centred and embedded in manufacturing workflows.
• Create clear metric definitions, calculation logic, data‑quality checks and documentation so that outputs are trusted, repeatable and scalable.
• Work with data engineers, solution architects, OT/IT teams and data scientists to define data pipelines, integration needs and fit‑for‑purpose analytical solutions.
• Support user acceptance testing, release readiness, training and adoption, and use feedback to improve the solution after deployment.

Operational Adoption

• Embed solutions into manufacturing workflows, decisions and ways of working by involving leaders and users throughout delivery.
• Support and drive the process, role and capability changes required for adoption, and address barriers to sustained use.
• Enable proven solutions to scale across relevant sites, processes and value streams while balancing global standards with legitimate local needs.
• Promote reuse, capture implementation learning and apply it to future deployments.

Stakeholder Engagement and Delivery

• Communicate progress, findings, risks and data limitations clearly to manufacturing leaders, subject‑matter experts and programme stakeholders.
• Plan and manage assigned analysis and delivery activities, dependencies and milestones within cross‑functional squads.
• Facilitate working sessions that connect operational knowledge with data evidence and ensure recommendations are practical for the manufacturing environment.
• Build analytical capability in operational teams through coaching, documentation and simple self‑service tools where appropriate.

Qualifications and Experience

Required Qualifications and Experience

• Bachelor’s degree in Engineering, Manufacturing, Supply Chain Management, Computer Science, Data Science, Information Systems, Business or a related field; advanced degree preferred.
• 6+ years developing complex, cross‑functional delivery of analytics, automation or AI‑enabled solutions in manufacturing or supply chain environments.
• Strong understanding of manufacturing processes, value streams and operational improvement opportunities.
• Sufficient technical depth to assess solution feasibility, data readiness, systems integration, scalability and delivery risk, and to work effectively with technical specialists.
• Experience with manufacturing data and technology environments such as ERP, MES, equipment or IoT data, historians or similar operational technology platforms.
• Experience working in cross‑functional agile or project delivery teams and supporting solutions through requirements, testing, deployment and adoption.

Preferred Qualifications and Experience

• Advanced degree in Engineering, Data Science, Analytics, Manufacturing, Supply Chain or a related discipline.
• Experience within Industry 4.0, smart manufacturing or large‑scale manufacturing transformation environments.
• Exposure to predictive or prescriptive analytics and experience building AI, data science or advanced analytics solutions.
• Experience supporting manufacturing excellence, Lean, Six Sigma or similar continuous‑improvement programmes.

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Joining us is more than saying “yes” to making the world a healthier place. It’s discovering a career that’s challenging, supportive and inspiring. Where a culture driven by excellence helps you not only meet your goals, but also create new ones. We focus on creating a diverse and inclusive culture, encouraging individual expression in the workplace and thrive on the innovative ideas this generates. If this sounds like the workplace for you, we commit to respond to every applicant.