Manufacturing Data Analytics Lead

ResMed (UK); Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

ResMed (UK) is seeking a Manufacturing Data Analyst to drive analytics, reporting and dashboards that boost visibility of manufacturing performance and deliver measurable operational and financial outcomes. The role collaborates with data science and engineering to support predictive and prescriptive insights across equipment, processes, quality, labour and materials.

The successful candidate will work across data sources, validate data, and deliver outputs with clear communication to both

Qualifications

• Bachelor's degree in Engineering, Manufacturing, Supply Chain Management, Computer Science, Data Science, Information Systems, Business or a related field; advanced degree preferred.
• 3+ years developing complex, cross-functional delivery of analytics, automation or AI-enabled solutions in manufacturing or supply chain environments.
• 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.

Responsibilities

• Execute analysis against agreed business questions, measures of success, data requirements and delivery priorities.
• Extract, prepare, combine and validate production, process, quality, downtime, cycle-time, labour, material-flow and cost data using approved sources and methods.
• Produce recurring and ad hoc analysis to identify loss drivers, trends, constraints, process variation and improvement opportunities.
• Apply established analytical methods, including data profiling, descriptive statistics, segmentation, root-cause analysis, statistical process control and time-series analysis.
• Build and maintain assigned dashboards, reports and visualisations using agreed metric definitions, calculation logic and design standards.
• Complete data reconciliation, quality checks and test evidence; document assumptions, exceptions and limitations before outputs are released.
• Present findings clearly and factually, distinguishing evidence from assumptions and escalating results that require deeper investigation or specialist review.
• Capture user feedback, monitor assigned solution usage and identify data, process or usability barriers to sustained adoption.
• Implement agreed improvements within the solution scope and elevate broader process, role or capability changes to the delivery lead.
• Create and maintain user guidance, training materials and standard work to support consistent use of deployed solutions.
• Contribute reusable analysis, documentation and lessons learned to support deployment of proven solutions in other value streams or sites.
• Participate in working sessions that connect operational knowledge with data evidence and translate agreed requirements into practical analytical outputs.
• Plan and complete assigned analysis, dashboard, testing and release-readiness activities within cross-functional delivery squads.
• Provide clear updates on assigned deliverables, data limitations, dependencies and risks to the delivery lead and relevant stakeholders.
• Communicate issues early, coordinate assigned actions and elevate decisions involving significant ambiguity, technical complexity or cross-site impact.

Education

Bachelor's degree in Engineering/Manufacturing/CS

Advanced degree preferred

Tools

ERP

MES

IoT data platforms

Historians

Job description

ResMed (UK) is seeking a Manufacturing Data Analyst to drive analytics, reporting and dashboards that boost visibility of manufacturing performance and deliver measurable operational and financial outcomes. The role collaborates with data science and engineering to support predictive and prescriptive insights across equipment, processes, quality, labour and materials.

The successful candidate will work across data sources, validate data, and deliver outputs with clear communication to both