Manufacturing Data Analyst
The Manufacturing Data Analyst plays a key role within ResMed’s Manufacturing Excellence Program. The role executes analysis, reporting and dashboard activities that improve visibility of manufacturing performance and support measurable operational and financial outcomes. 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.
Let’s talk about Responsibilities
Manufacturing Data Analysis and Insights
- 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.
Operational Adoption
- 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 escalate 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.
Stakeholder Engagement and Delivery
- 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 escalate decisions involving significant ambiguity, technical complexity or cross-site impact.
Let’s talk about 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.
- 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.
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, apply now! We commit to respond to every applicant.
