Job Description

As a Decision Support Data Scientist within the Manufacturing Analytics and Strategy Execution (MASE) team, you will own the quality and integrity of insights that support senior-management decision-making and performance management. You will combine decision science, statistics, forecasting, advanced analytics, machine learning, AI, and business understanding to strengthen manufacturing performance and enable faster, more consistent enterprise decisions.

As the analytical engine behind the Global Manufacturing Control Tower, you will translate operational data into trustworthy performance narratives, driver analyses, forecasts, scenarios, early-warning signals, and recommendations. You will ensure that analytical evidence is reliable, explainable, and relevant to the decisions leaders need to make.

You will focus on defining the right questions, metrics, methods, and assumptions to help leaders understand what is happening, why it is happening, what may happen next, and what actions should be considered.

Principal Duties / Responsibilities

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
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.
Build and validate analytical prototypes and models using Python, SQL, R, BI tools, and cloud technologies document methods, assumptions, limitations, and validation results to support reproducibility and responsible use.
Monitor analytical performance after deployment, including forecast accuracy, bias, stability, drift, data quality, and continued business relevance recommend recalibration, enhancement, or replacement when required.
Partner with Analytics, Project Lead, Engineering team and Operation & Management team to align analytics with operational priorities, global standards, and enterprise decision processes.
Lead the analytical workstream from problem framing through validation and value assessment, supporting cross-functional alignment and adoption while the Analytics Lead retains accountability for end-to-end Control Tower delivery and user experience.

Qualifications

Bachelor's or Master's degree in Data Science, Statistics, Operations Research, Computer Science, Engineering, Business Analytics, or a related field.
At least 8 years of experience in data science, decision science, manufacturing analytics, business analytics, or equivalent work experience.
Strong analytical and problem-solving skills, with demonstrated ability to frame complex decisions, test hypotheses, challenge assumptions, and translate data into actionable recommendations.
Strong experience producing executive-level performance insights, decision-support analyses, forecasts, scenarios, and management narratives for senior stakeholders.
Strong hands-on experience in statistics, forecasting, scenario modeling, diagnostic and predictive analytics, machine learning, and AI, with practical application in manufacturing or operations.
Strong experience defining and validating KPIs, baselines, targets, thresholds, and leading indicators, and assessing uncertainty, accuracy, bias, stability, explainability, and business relevance.
Excellent written and verbal communication skills, with the ability to explain analytical methods, limitations, implications, and recommendations to non-technical stakeholders and senior leaders.
Proficiency in programming and analytical languages such as Python, SQL, or R.
Experience using enterprise data-visualization tools such as Tableau, Power BI, Qlik, Spotfire, or similar to communicate insights and support decision-making.
Ability to assess and work with complex enterprise data, including data quality, semantic consistency, lineage, and limitations affecting analytical conclusions.
Demonstrated ability to lead cross-functional analytical workstreams with disciplined planning, stakeholder alignment, peer review, and value assessment.
Ability to operate under pressure and deliver rapid analytical prototypes or MVPs to validate concepts, de-risk assumptions, and confirm direction.

Preferred Qualifications

Experience in manufacturing operations, industrial engineering, quality management, supply chain, finance, or enterprise performance management.
Experience supporting Control Tower, decision-intelligence, performance-management, IBP/S&OP, enterprise planning, or operational-intelligence initiatives.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Experience integrating and interpreting data from ERP, MES, shopfloor systems, enterprise data platforms, or operational workflows.
Familiarity with Industry 4.0 technologies, including IIoT, digital twins, automation.
PMP, Scrum Master, or relevant analytics certification preferred, with experience supporting large-scale, enterprise-level initiatives.

Decision Support Data Scientist (Global Manufacturing Analytics)