Apply business analytics, statistics, and emerging AI/ML capabilities to identify operational opportunities, enhance decision-making, and integrate forecasting, scenario modeling, and predictive insights into analytics products.
Drive enterprise adoption and value realization through stakeholder engagement, communication, training, and change management, ensuring analytics products become embedded in day-to-day operations and management routines.
Continuously improve the analytics portfolio by monitoring product adoption, user feedback, business outcomes, and emerging technologies, while promoting best practices and standardization across the global manufacturing network.
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Partner with Synapxe and Group Digital Health to support the development, testing, implementation, and enhancement of the Enterprise Data Warehouse and Analytics Workbench.
Drive platform governance, optimisation, and future roadmap planning to ensure scalability, performance, and sustainability.
Champion data accessibility while maintaining strong governance, quality, and security standards.
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a. Perform in-depth analysis (including data extraction, processing, and analysing), formulate insights and provide recommendations that support the desired project outcomes and business case.
b. Develop and implement solutions using Generative AI techniques and leverage data visualization tools such as Power BI or Tableau to create intuitive and impactful dashboards.
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Integrate and collate data silos in a manner that is both scalable and compliant.
Collaborate with Project Managers, Data Architects, Business Analysts, Frontend Developers, Designers, and Data Analysts to build scalable, data-driven products.
Be responsible for developing backend APIs and working on databases to support applications.
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Integrate and collate data silos in a manner that is both scalable and compliant.
Collaborate with Project Managers, Data Architects, Business Analysts, Frontend Developers, Designers, and Data Analysts to build scalable, data-driven products.
Be responsible for developing backend APIs and working on databases to support applications.
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Support the definition and validation of KPI logic, baselines, targets, thresholds, and leading indicators, and help document how measures should be calculated and interpreted.
Apply data exploration, statistics, forecasting, diagnostic analytics, machine learning, and AI methods with guidance, selecting approaches that are appropriate for the business question.
Prepare clear visualisations, performance narratives, driver analyses, early-warning signals, and scenario implications that distinguish meaningful signals from noise and highlight areas requiring attention.
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Define and validate KPI logic, baselines, targets, thresholds, and leading indicators, ensuring consistent calculation, appropriate interpretation, and relevance to management decisions.
Select and apply statistics, forecasting, diagnostic analytics, machine learning, and AI based on the business question; quantify uncertainty, confidence, bias, stability, sensitivity, and decision trade-offs.
Produce executive performance narratives, driver analyses, early-warning signals, scenario implications, and evidence-based recommendations, distinguishing meaningful signals from noise and clarifying where management attention is required.
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LLM Workflow Engineering: Build, evaluate, and optimize LLM-based workflows, including prompting, retrieval-augmented generation (RAG), inference orchestration, benchmarking, and quality evaluation.
Machine Learning Production: Develop and productionize machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support.
Distributed Data Processing: Implement robust data processing techniques such as data cleansing, outlier detection, and missing-data handling using distributed or large-scale frameworks (e.g., PySpark, BigQuery).
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