Decision Support Data Scientist (Global Manufacturing Analytics)

Job Description As a Decision Support Data Scientist within the Manufacturing Analytics and Strategy Execution (MASE) team, you will contribute to the development and validation of high-quality insights that support management decision-making and performance management. You will apply decision science, statistics, forecasting, advanced analytics, machine learning, AI, and business understanding to help strengthen manufacturing performance and enable faster, more consistent decisions. Working with experienced team members as part of the Global Manufacturing Control Tower, you will translate operational data into clear performance insights, driver analyses, forecasts, scenarios, early-warning signals, and recommendations. You will help ensure that analytical outputs are reliable, explainable, and relevant to the decisions leaders need to make. This role is designed for an early-career professional who is curious, analytical, and eager to learn. You will build practical experience in manufacturing analytics while gradually taking ownership of defined decision-support use cases and contributing to the question: what is happening, why is it happening, what may happen next, and what actions should be considered? Principal Duties / Responsibilities • 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 • 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. • Build and test analytical prototypes using Python, SQL, R, BI tools, and cloud technologies; document methods, assumptions, limitations, and validation results to support reproducibility and responsible use. • Assess data quality and monitor analytical outputs, including forecast accuracy, bias, stability, drift, and continued business relevance; flag issues and support model improvement when required. • Collaborate with the Analytics Lead, experienced Data Scientists, Engineering teams, the Manufacturing Strategy & Execution Lead, and operational stakeholders to align insights with business priorities and Control Tower deliverables. • Participate in analytical workstreams from problem framing through validation and value assessment, incorporating feedback and gradually taking ownership of defined analyses and decision-support use cases. Qualifications • Bachelor's or Master's degree in Data Science, Statistics, Operations Research, Computer Science, Engineering, Business Analytics, Economics, or a related quantitative field. A Master's degree is preferred. • One to two years of relevant work, internship, research, or project experience in data science, decision science, analytics, business intelligence, or a related field. • Strong foundation in analytical problem solving, with the ability to break down questions, test assumptions, interpret results, and communicate conclusions clearly. • Practical experience with data cleaning, exploratory data analysis, data visualisation, and preparation of analytical findings through internships, academic research, capstone projects, or work experience. • Foundational knowledge of statistics, forecasting, scenario modeling, diagnostic or predictive analytics, machine learning, and AI, with interest in applying these methods to business and operational problems. • Understanding of KPI design, baselines, targets, thresholds, leading indicators, and basic methods for assessing accuracy, bias, stability, explainability, and business relevance. • Good written and verbal communication skills, with the ability to explain analytical methods, limitations, findings, and implications to both technical and non-technical audiences. • Practical programming experience in Python, SQL, or R gained through coursework, internships, research, personal projects, or employment. • Experience using data-visualisation tools such as Tableau, Power BI, Qlik, Spotfire, or similar to communicate insights and support decision-making. • Ability to work with imperfect or complex data, identify data-quality limitations, and maintain attention to detail in analytical work. • Curiosity, willingness to learn, openness to feedback and peer review, and the ability to collaborate effectively in a global, cross-functional environment. • Ability to manage multiple priorities and deliver well-structured analyses or prototypes in a fast-paced environment. Preferred Qualifications • Internship, research, or project experience related to manufacturing, supply chain, quality, finance, operations, or enterprise performance management. • Coursework or project experience in decision science, operations research, optimisation, time-series forecasting, or scenario analysis. • Exposure to cloud platforms such as AWS, Azure, or Google Cloud, or to modern enterprise data platforms. • Experience analysing data from ERP, MES, shopfloor systems, business systems, or other operational datasets. • Famil…