Data Scientist

Polaris Inc.•Plymouth, MN

1d•$87,000 - $115,000•Hybrid

About The Position

As a Data Scientist, you will apply advanced analytics, statistical modeling, and data science techniques to solve complex business problems and support data-driven decision-making across Polaris. In this role, you will focus on the Supply Chain organization, optimizing decisions related to Supplier Risk Mitigation by transforming large and varied datasets into actionable insights. You will collaborate with cross-functional partners to identify opportunities, build predictive models, and communicate results clearly to both technical and non-technical audiences. You will also help deploy analytics solutions into production while maintaining high standards for data quality and solution performance. This role offers the opportunity to shape critical business decisions by developing impactful, end-to-end analytics and technology solutions.

Requirements

Bachelor’s degree in Applied Statistics, Mathematics, Data Science, Computer Science, or a related field.
Minimum of 2 years of professional experience in a predictive analytics-focused role, excluding internships and academic coursework.
Demonstrated knowledge of statistical methods and predictive/analytical modeling techniques and practices.
Experience using data mining and predictive modeling tools such as SQL, R, Python, and Snowflake.
Experience creating data visualizations using tools such as Tableau or Power BI.
Experience preparing, rationalizing, and processing data in a cloud environment.
Demonstrated ability to build cross-functional relationships and frame analytical results within a business context.
Strong organizational skills, attention to detail, and ability to manage multiple projects and priorities.
Strong verbal and written communication skills, including the ability to partner effectively with non-technical stakeholders.

Responsibilities

Partner with business stakeholders to understand complex challenges and develop end-to-end data-driven solutions.
Extract, cleanse, integrate, and prepare large datasets from multiple systems and sources for analysis.
Perform exploratory and targeted analyses using a variety of statistical methods, including clustering, regression, decision tree/random forest, time series, and neural networks.
Develop, validate, and optimize predictive and analytical models to generate actionable insights and recommendations.
Collaborate cross-functionally to translate analytical findings into measurable business improvements.
Communicate technical analyses, insights, and recommendations effectively to both technical and non-technical audiences.
Integrate and deploy model results into production across cloud and edge computing platforms.
Ensure data quality, model performance, and solution integrity throughout the development process.
Document methodologies, findings, and results using established templates to support knowledge sharing and reuse.
Gather feedback from business users to continuously improve data science products.