Own the Manufacturing Control Tower insight portfolio, defining the product roadmap, prioritizing capabilities, and continuously evolving analytics products that improve operational visibility, decision intelligence, and business execution.
Lead the design and delivery of executive-ready dashboards, KPI frameworks, and analytics solutions, ensuring trusted metrics, intuitive user experience, and actionable insights that support strategic and operational decision-making.
Translate business priorities into scalable global analytics solutions by partnering with Manufacturing, Engineering, and cross-functional teams to define requirements, prioritize opportunities, and deliver high-impact digital capabilities.
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Bachelor's degree in Computer Science, Data Science, Business Analytics, Applied Mathematics, Statistics, Operations Research, Engineering, Artificial Intelligence, or related quantitative disciplines.
Minimum of 2 – 3 years in service industry/health-care related areas preferred.
Strong acumen for data analysis and proficiency with utilising various analytical and modelling tools.
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The Senior Assistant Manager will be part of the Analytics team who partner stakeholders to interpret and present statistical outcomes to support improvement initiatives. The individual collaborates across departments to address their challenges using data and appropriate techniques in Data Science, Optimisation and Machine Learning.
Support the development and validation of Control Tower insights, helping ensure that analyses, forecasts, scenarios, and recommendations are accurate, explainable, and supported by appropriate evidence.
Work with senior Data Scientists and business stakeholders to translate operational questions into clear analytical tasks, assumptions, hypotheses, data requirements, and success criteria.
Develop decision-support analyses and performance insights across: productivity, throughput, and capacity quality, yield, and operational risk delivery, shipment, and backlog performance cost, margin, and resource drivers manufacturing network and site performance
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Own the analytical quality and integrity of Control Tower insights, ensuring conclusions, forecasts, scenarios, and recommendations are accurate, explainable, decision-relevant, and supported by appropriate evidence.
Structure ambiguous business questions into clear decision problems by defining the decisions, options, assumptions, hypotheses, evidence, and success criteria required for analysis.
Develop Control Tower decision-intelligence and performance insights across: productivity, throughput, and capacity quality, yield, and operational risk delivery, shipment, and backlog performance cost, margin, and resource drivers manufacturing network and site performance
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GenAI System Development: Design, build, and improve GenAI-powered and agentic systems supporting semiconductor engineering workflows such as code generation, data extraction, analytics, documentation automation, failure triage, and technical knowledge retrieval.
Large-Scale Data Pipelines: Develop scalable data pipelines and analytical workflows to ingest, clean, transform, and analyze large, complex, and heterogeneous datasets from multiple manufacturing and engineering systems.
Advanced Data Analytics: Apply Python, SQL, and data science libraries (e.g., pandas, matplotlib) to perform deep analysis, generate visualizations, and deliver actionable engineering insights.
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