Data Science Intern - Summer 2027

Post Consumer Brands•Lakeville, MN

1d•Onsite

About The Position

This 12-week summer experience is designed to provide meaningful work, professional development, and hands-on exposure to enterprise data science and analytics solutions. As a Data Science Intern, you'll work alongside experienced data professionals to uncover insights, solve business challenges, and develop analytics solutions that drive decision-making across the organization. You'll gain experience with technologies including Python, Snowflake, Snowpark ML, Snowflake Cortex, dbt, SQL, and data visualization tools while contributing to projects that create real business value.

Requirements

Pursuing a bachelor's or master's degree in Data Science, Statistics, Mathematics, Computer Science, Analytics, Engineering, or a related field, with an expected graduation date between December 2027 and August 2028.
Strong analytical, problem-solving, and communication skills, with a natural curiosity to understand the "why" behind business challenges.
Experience with Python, R, SQL, or similar analytical tools, along with familiarity with Power BI, Tableau, Excel, or other data visualization platforms.
Understanding of statistical modeling, machine learning, or predictive analytics concepts.
Ability to work independently and collaborate effectively within a team environment.
Must be authorized to work in the United States without current or future visa sponsorship.

Nice To Haves

Previous internship, work, leadership, research, or project experience.
Experience with platforms such as Snowflake, Databricks, Hadoop, or similar technologies.
Interest in the consumer packaged goods or manufacturing industry and a desire to explore career opportunities with Post Consumer Brands after graduation.

Responsibilities

Develop and enhance machine learning and statistical models.
Analyze enterprise data to identify trends, opportunities, and insights.
Create dashboards, visualizations, and analytical tools for business stakeholders.
Partner with cross-functional teams to solve complex business challenges.
Support the development of scalable analytics and data science solutions.
Build, test, and optimize machine learning models.
Engineer new features and data sets to improve model performance.
Evaluate internal and external data sources for new analytics opportunities.
Conduct analyses to address key business questions and strategic initiatives.
Develop dashboards and visualizations that communicate actionable insights.
Use Python, SQL, and modern analytics tools to generate recommendations.