Data Analyst, Manufacturing

Manufacturing Data Analyst The Manufacturing Data Analyst plays a key role within ResMed’s Manufacturing Excellence Program.

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.

About Resmed

• 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.

Responsibilities

• 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.
• 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.
• 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.

Requirements

• 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.

If this sounds like the workplace for you, apply now! We commit to respond to every applicant.