Data Science Engineering Intern

Emerson•Boulder, CO

21h•$24 - $28•Onsite

About The Position

This internship role focuses on developing predictive models and data visualization tools to improve supply chain planning and operations. The intern will work on projects such as creating a Power BI dashboard for backlog prediction, building weekly forecast models, developing a surge capacity model for specific product lines, and applying data science methodologies to translate complex operational data into actionable insights. The role involves collaboration with cross-functional teams and participation in the full data science project lifecycle within a manufacturing context.

Requirements

Pursuing a bachelor’s degree in engineering
Zero (0) years of related experience
Ability to work full-time (40 hours) per week in-person
Legal authorization to work in the United States

Nice To Haves

Experience with Power BI, Tableau, or similar data visualization platforms
Familiarity with Python, R, SQL, or statistical modeling techniques
Prior internship or project experience in data analytics, operations research, or supply chain
Exposure to manufacturing, supply chain planning, or ERP/MES systems

Responsibilities

Develop a Power BI dashboard that predicts future past-due backlog based on Customer Required Date (CRD), Planned Ship Date (PSD), model code, value stream, and material status, enabling planners to answer, "What will be late next month?" with data-driven confidence.
Build weekly projected late backlog forecasts, top model code risk heat maps, and simulation tools that quantify the impact of material availability, capacity constraints, and scheduling decisions on backlog recovery.
Create a predictive surge capacity model for low-volume product lines (e.g., CMFHC) that identifies demand mix variation, highlights bottlenecks by operation, and produces growth scenario analyses to guide investment prioritization and lead time stability.
Apply data science methodologies, including statistical modeling, forecasting, and visualization, to translate complex operational datasets into actionable insights for planners, operations leaders, and SIOP discussions.
Collaborate with cross-functional teams including supply chain planning, operations, and engineering to validate models, gather requirements, and ensure analytical tools align with business priorities and decision-making workflows.
Participate in multiple analytical projects at various stages, from data exploration and model development to dashboard deployment and user training, gaining a comprehensive understanding of the data science project lifecycle in a manufacturing context.