Data Scientist Intern

Hercules Industries•Denver, CO

9h•Onsite

About The Position

The Data Scientist Intern at Hercules Industries will play an active role in building a modern, data-driven enterprise. This is not a traditional internship focused on observation or support work; interns will work on real business problems that directly impact supply chain, operations, and customer outcomes. As part of Hercules’ transformation to a data-centric organization, this role provides the opportunity to apply data science to real-world industrial challenges, contribute to meaningful value creation, and gain direct exposure to business leaders and decision-making. Interns will be expected to think critically, work collaboratively, and deliver insights that drive action.

Requirements

Currently pursuing a bachelor’s or master’s degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or related discipline.
Strong foundational knowledge of statistics and probability.
Strong foundational knowledge of data analysis and exploratory data analysis (EDA).
Proficiency in at least one programming language commonly used in data science (e.g., Python or R).
Experience working with data using tools such as SQL, Pandas, or similar frameworks.
Ability to structure ambiguous problems into clear analytical approaches.
Strong attention to detail and commitment to data accuracy and quality.
Effective communication skills, with the ability to explain insights to both technical and non-technical audiences.
Ability to effectively communicate with customers and staff to make an accurate assessment of customer needs.
Ability to perform basic mathematical calculations required to accurately complete assigned tasks.
Intermediate computer skills, including Microsoft Office.
Ability to interpret a variety of instructions furnished in oral or written form.
Ability to use sound judgment and problem-solving skills.

Responsibilities

Work on clearly defined, high-impact business problems such as: Demand forecasting and demand variability analysis, Inventory optimization and parameter tuning (safety stock, lead times), Service level and fill rate analysis, Supplier performance and lead time variability.
Break down business problems into structured analytical approaches.
Ask thoughtful questions to refine problem statements and assumptions.
Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
Build analytical models using statistical and machine learning techniques (as appropriate to experience level).
Develop data visualizations and summaries that communicate key findings.
Support the development of predictive and descriptive models alongside full-time data scientists.
Work with datasets from ERP (M3), data warehouse, and operational systems.
Clean, structure, and validate data for analysis.
Identify data quality issues and highlight opportunities for improvement.
Collaborate with Data Engineers to understand data pipelines and structures.
Translate analytical findings into clear, actionable insights.
Present results to team members and, where appropriate, business stakeholders.
Connect analysis to real business outcomes (e.g., inventory reduction, improved service levels).
Support the development of recommendations that can be implemented in the business.
Work closely with: Data Scientists, Data Engineers, Supply Chain and Operations leaders.
Participate in regular project reviews and working sessions.
Actively seek feedback and continuously improve technical and business skills.