Digital & IT - Predictive Data Science Internship - Summer

At Polaris Inc., we have fun doing what we love by driving change and innovation. We empower employees to take on challenging assignments and roles with an elevated level of responsibility in our agile working environment. Our people make us who we are, and we create incredible products and experiences that empower us to Think Outside.

The Polaris D&IT Predictive Data Science Internship is a 12-week paid summer internship designed for emerging juniors and seniors interested in applying data science, analytics, and AI to solve real business challenges within the global powersports industry.

As a Predictive Data Science Intern, you will work with large datasets to uncover insights, build predictive models, and develop data-driven recommendations that support business decisions across Polaris. Projects may include analyzing customer, dealer, supply chain, and operational data to identify trends, improve forecasting, optimize processes, and drive business performance.

You will partner with cross-functional teams across analytics, technology, operations, and the business to turn data into actionable solutions and meaningful outcomes.

• Predictive analytics and forecasting
• Machine learning and AI solutions
• Data management and engineering
• Business intelligence, data visualization, and decision support

Internship

Location:

D&IT interns are typically based at Polaris Headquarters in Medina, MN, where they collaborate closely with technology, analytics, and business teams across the organization.

As a Polaris intern it is important that you embrace the adventure and remain open to any placement opportunity. By being adaptable, you will gain diverse experiences and develop a broader skill set, which are essential for your professional development and success within Polaris.

Our internship program is crafted to build the foundational skills and experiences essential for your career success. Upon completing the internship, those who demonstrate exceptional performance and a strong passion for learning may be offered a returning internship or a position in our rotational leadership development program.

• Mentorship: you are paired with one of our DP associates for mentor support throughout your 12-weeks.
• Programming
  
  Hours:
  
  weekly functional and centralized learning sessions to broaden your knowledge base, both within and beyond your program.
• Networking: intentional time with peers and leaders at Polaris to build your professional network.
• Intern Innovation Challenge: internal case study designed to promote cross functional development where you and your team will present creative and practical solutions to a real ‘Polaris’ problem to our executive leadership.
• Early Talent Summit Week: participate in our 2-day event in our Wyoming, MN office. This immersive experience brings together all US interns and DPs for professional development, structured networking, a DP graduation event, inspiring executive speakers, and a thrilling team ride on some of your favorite Polaris vehicles.
• Community Engagement: opportunity to get involved in your local community.
• Final Presentations: present to our senior level leaders to showcase your career aspirations and recap your internship experience.

Applications are reviewed on a rolling basis throughout the fall. Selected candidates will be invited to participate in a phone screen followed by a virtual interview with Polaris HR leaders. Candidates who successfully complete the interview process may receive an internship offer. Internship assignments are finalized in February based on candidate interests, strengths, and business needs.

Polaris provides relocation support for eligible interns working 35+ miles from their assigned location. Benefits may include a relocation stipend and assistance with securing temporary housing accommodations. Interns are responsible for daily transportation to and from the office.

• Pursuing a Bachelor's/Master’s degree in Data Science, or other related business degree with an intended graduation date between…