Skill

Principal Component Analysis

Data Science, Analytics and AI/ML

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of large datasets by transforming correlated variables into a smaller set of uncorrelated variables called principal components, which capture the most variance in the data. It's widely used by data scientists and researchers for data visualization, noise reduction, and as a preprocessing step before machine learning modeling. PCA is a foundational tool in fields like genomics, finance, and image processing.

Open roles requiring Principal Component Analysis (1)

Industrial R&D Statistics & Experimentation Manager

General Mills

Full-time · Minneapolis, MN · $128,900 – $193,400

Lead a statistics and data science team of five within a food company's R&D organization, balancing people management with hands-on work on experiment design, statistical modeling, and decision-making support across product and process development. This role suits experienced statisticians who want to grow into leadership while maintaining technical credibility and influence across a matrixed organization.

Listed on General Mills’s careers site · Apply there ↗

• Feature Engineering
• Dimensionality Reduction
• Linear Algebra
• Multivariate Analysis
• Unsupervised Machine Learning