Skill
Dimensionality Reduction
Data Science, Analytics and AI/ML
Dimensionality reduction is a set of techniques in machine learning and statistics used to reduce the number of variables in a dataset while preserving as much meaningful information as possible, using methods like Principal Component Analysis (PCA), t-SNE, or UMAP. Data scientists use it to simplify complex datasets, speed up model training, reduce noise, and visualize high-dimensional data in two or three dimensions.
Open roles requiring Dimensionality Reduction (2)
Software Development Engineer
Intel
Full-time
This role applies machine learning and data analytics to solve manufacturing problems in semiconductor production, working with engineers to develop tools and insights from factory data. It suits recent graduates with strong statistics or computer science foundations and hands-on experience in predictive modeling and data programming.
Listed on Intel’s careers site · Apply there ↗
Senior/Staff Data Scientist, Analytics
Bluefish
New York, NY
Bluefish is seeking a statistical expert to lead experimentation and causal inference work across their AI marketing platform, owning the rigor behind data-driven decisions across product, marketing, and operations. This role offers the chance to build analytics practices from scratch at a well-funded startup in the AI-driven martech space.
Listed on Bluefish’s careers site · Apply there ↗
Roles that use Dimensionality Reduction
Related skills
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