{"id":3480767,"url":"https://alion.io/job/resmed-senior-data-analyst-manufacturing","title":"Senior Data Analyst - Manufacturing","company":{"id":99683,"name":"ResMed","domain":"resmed.co.uk","url":"https://alion.io/company/resmed-2","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":79,"open_postings":47,"ghost_share":0.021,"stale_share":0.745,"repost_share":0.043,"time_to_fill_p50_days":38,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":106000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":49},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Statistical Process Control","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-10-29T20:06:02Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-01T17:02:40Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Senior Manufacturing Data Analyst\nThe 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.\nThe 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.\nSuccess 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.\nLet’s talk about Responsibilities\nManufacturing Data Products and Insights\nPartner with value stream owners, site teams, engineers and frontline users to define manufacturing problems, analytical questions, hypotheses, measures of success and data requirements. \nAnalyse production, process, quality, downtime, cycle-time, labour, material-flow and cost data to identify loss drivers, trends, constraints, process drift and improvement opportunities. \nEnable near-real-time manufacturing performance visibility, including part-level production data, OEE and performance, availability and quality loss diagnostics. \nApply appropriate analytical methods including data profiling, descriptive statistics, correlation, segmentation, root-cause analysis, statistical process control and time-series analysis. \nTranslate complex analysis into practical operational insights, clear recommendations and prioritised actions. \nDevelop, test and maintain dashboards, reports, analytical models and decision-support tools that are reliable, user-centred and embedded in manufacturing workflows. \nCreate clear metric definitions, calculation logic, data-quality checks and documentation so that outputs are trusted, repeatable and scalable. \nWork with data engineers, solution architects, OT/IT teams and data scientists to define data pipelines, integration needs and fit-for-purpose analytical solutions. \nSupport user acceptance testing, release readiness, training and adoption, and use feedback to improve the solution after deployment. \nOperational Adoption\nEmbed solutions into manufacturing workflows, decisions and ways of working by involving leaders and users throughout delivery. \nSupport and drive the process, role and capability changes required for adoption, and address barriers to sustained use. \nEnable proven solutions to scale across relevant sites, processes and value streams while balancing global standards with legitimate local needs. \nPromote reuse, capture implementation learning and apply it to future deployments. \nStakeholder Engagement and Delivery\nCommunicate progress, findings, risks and data limitations clearly to manufacturing leaders, subject-matter experts and programme stakeholders. \nPlan and manage assigned analysis and delivery activities, dependencies and milestones within cross-functional squads. \nFacilitate working sessions that connect operational knowledge with data evidence and ensure recommendations are practical for the manufacturing environment. \nBuild analytical capability in operational teams through coaching, documentation and simple self-service tools where appropriate. \nLet’s talk about Qualifications and Experience\n\nRequired Qualifications and Experience\nBachelor’s degree in Engineering, Manufacturing, Supply Chain Management, Computer Science, Data Science, Information Systems, Business or a related field; advanced degree preferred. \n6+ years developing complex, cross-functional delivery of analytics, automation or AI-enabled solutions in manufacturing or supply chain environments. \nStrong understanding of manufacturing processes, value streams and operational improvement opportunities. \nSufficient technical depth to assess solution feasibility, data readiness, systems integration, scalability and delivery risk, and to work effectively with technical specialists. \nExperience with manufacturing data and technology environments such as ERP, MES, equipment or IoT data, historians or similar operational technology platforms. \nExperience working in cross-functional agile or project delivery teams and supporting solutions through requirements, testing, deployment and adoption. \nPreferred Qualifications and Experience\nAdvanced degree in Engineering, Data Science, Analytics, Manufacturing, Supply Chain or a related discipline. \nExperience within Industry 4.0, smart manufacturing or large-scale manufacturing transformation environments. \nExposure to predictive or prescriptive analytics and experience building AI, data science or advanced analytics solutions. \nExperience supporting manufacturing excellence, Lean, Six Sigma or similar continuous-improvement programmes. \n#LI-APAC\nJoining 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! 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