Data Scientist - Advanced Manufacturing

Vertiv•St. Louis, MO

About The Position

Global Footprint & Capacity Data Analysis Design, build, and maintain scalable analytical frameworks and data models that enable rapid, data-driven decision making related to capacity allocation, footprint strategy, and growth planning. Develop constraint detection logic, capacity utilization metrics, and early-warning indicators to proactively surface risks before they impact customer commitments. Translate complex, multi-source datasets into clear insights, scenarios, and recommendations for executive and customer-facing decision making. Build and maintain capacity, throughput, and utilization models across multiple product lines to identify constraints, bottlenecks, and scaling limits. Conduct scenario analysis, sensitivity modeling, and what-if simulations to optimize plant loading, equipment utilization, and capital investment decisions. Partner with regional and site teams to translate demand forecasts and variability into capacity insights, enabling proactive constraint management. Capacity Planning & Optimization Build and maintain capacity, throughput, and utilization models across multiple product lines to identify constraints, bottlenecks, and scaling limits. Conduct scenario analysis, sensitivity modeling, and what-if simulations to optimize plant loading, equipment utilization, and capital investment decisions. Partner with regional and site teams to translate demand forecasts and variability into capacity insights, enabling proactive constraint management. Cross-Functional & Global Analytics Collaboration Collaborate with Engineering, Operations, Supply Chain, and Finance to define analytical assumptions, data definitions, and decision criteria for strategic manufacturing initiatives. Coordinate across global teams to standardize analytics methodologies, models, and planning tools for footprint and capacity analysis. Drive regional alignment and standardization of global data analytics best practices to support consistency and drive process maturity Data-Driven Decision Intelligence Leverage and integrate data from MES, ERP, capacity planning tools, and external sources to support strategic manufacturing analysis. Design and maintain cost, utilization, and capacity models that enable fast, repeatable scenario evaluation and executive decision making. Translate complex datasets into clear insights, visualizations, and recommendations for senior leadership and customer-facing discussions. Process Standardization & Scalable Analytics Enable scalable manufacturing strategies by embedding analytics into modular BoP approaches and digital factory initiatives. Support new product introductions by applying data-driven manufacturability and capacity-readiness assessments early in the design and planning process. Continuously improve analytical frameworks to enhance speed, accuracy, and consistency of global footprint and capacity decisions.Global Footprint & Capacity Data Analysis Design, build, and maintain scalable analytical frameworks and data models that enable rapid, data-driven decision making related to capacity allocation, footprint strategy, and growth planning. Develop constraint detection logic, capacity utilization metrics, and early-warning indicators to proactively surface risks before they impact customer commitments. Translate complex, multi-source datasets into clear insights, scenarios, and recommendations for executive and customer-facing decision making. Build and maintain capacity, throughput, and utilization models across multiple product lines to identify constraints, bottlenecks, and scaling limits. Conduct scenario analysis, sensitivity modeling, and what-if simulations to optimize plant loading, equipment utilization, and capital investment decisions. Partner with regional and site teams to translate demand forecasts and variability into capacity insights, enabling proactive constraint management.

Requirements

Bachelor’s Degree in: Data Science/Analytics/Statistics, Computer Science, Industrial Engineering, Operations Research, Manufacturing Engineering, Supply Chain / Logistics, Mathematics, Physics, or Economics (quantitative focus)
3-5+ years of experience in data analysis, manufacturing analytics, capacity planning, or operations strategy.
Proven experience with global manufacturing operations, footprint optimization, or capacity modeling.
Experience building analytical frameworks, scenario models, or data-driven decision tools for executives or cross-functional teams.
Demonstrated ability to work effectively in a cross-functional environment across a diverse, global team
Strong proficiency in AI Agent building/modeling, SQL, Python, R, or other data analysis tools.
Experience with ERP/MES systems, BI tools (Power BI, Tableau), and capacity planning software.
Expertise in statistical analysis, scenario modeling, forecasting, optimization, and KPIs for manufacturing or supply chain.

Responsibilities

Design, build, and maintain scalable analytical frameworks and data models that enable rapid, data-driven decision making related to capacity allocation, footprint strategy, and growth planning.
Develop constraint detection logic, capacity utilization metrics, and early-warning indicators to proactively surface risks before they impact customer commitments.
Translate complex, multi-source datasets into clear insights, scenarios, and recommendations for executive and customer-facing decision making.
Build and maintain capacity, throughput, and utilization models across multiple product lines to identify constraints, bottlenecks, and scaling limits.
Conduct scenario analysis, sensitivity modeling, and what-if simulations to optimize plant loading, equipment utilization, and capital investment decisions.
Partner with regional and site teams to translate demand forecasts and variability into capacity insights, enabling proactive constraint management.
Collaborate with Engineering, Operations, Supply Chain, and Finance to define analytical assumptions, data definitions, and decision criteria for strategic manufacturing initiatives.
Coordinate across global teams to standardize analytics methodologies, models, and planning tools for footprint and capacity analysis.
Drive regional alignment and standardization of global data analytics best practices to support consistency and drive process maturity
Leverage and integrate data from MES, ERP, capacity planning tools, and external sources to support strategic manufacturing analysis.
Design and maintain cost, utilization, and capacity models that enable fast, repeatable scenario evaluation and executive decision making.
Translate complex datasets into clear insights, visualizations, and recommendations for senior leadership and customer-facing discussions.
Enable scalable manufacturing strategies by embedding analytics into modular BoP approaches and digital factory initiatives.
Support new product introductions by applying data-driven manufacturability and capacity-readiness assessments early in the design and planning process.
Continuously improve analytical frameworks to enhance speed, accuracy, and consistency of global footprint and capacity decisions.