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Principal Component Analysis Jobs (NOW HIRING)

Ra

SAS Analytics & Modeling- Banking

Jersey City, NJ · On-site

$100K/yr

You've experience working with Machine Learning techniques (Support Vector Machines, Genetic Algorithms, Random Forests, K-Nearest Neighbor algorithm, Principal Component analysis etc.) * You've more ...

Core One

Geospatial Data Scientist (Expert)

Gordon, GA · On-site

Demonstrated experience and proficiency with data science techniques including (but not limited to) logistic regression, vector, natural language processing, principal component analysis, clustering ...

RathRecruiting

Product Analytics Manager

San Jose, CA · On-site

Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Neural Networks, Clustering, Principal Component Analysis, Factor analysis, etc * Demonstrated ...

Workila

Modellor

Jersey City, NJ

... Principal Component analysis etc.) Minimum 3 years of experience in building models (data cleaning, dependent variable selection, independent variable study and understanding, variable reduction ...

TransUnion

Senior Data Analyst

Chicago, IL · On-site

$88K - $111K/yr

... principal component analysis, scenario and sensitivity analysis) as role develops. * You will support the writing of programs for data extraction, segmentation and statistical analysis on large ...

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Principal Component Analysis information

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$36.5K

$109.4K

$182K

How much do principal component analysis jobs pay per year?

As of Jun 20, 2026, the average yearly pay for principal component analysis in the United States is $109,393.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $125,000.00 per year, depending on experience, location, and employer.

What is the difference between Principal Component Analysis vs Data Scientist?

AspectPrincipal Component AnalysisData Scientist
Primary FocusData reduction and feature extractionData analysis, modeling, and insights
Required SkillsStatistics, linear algebra, programmingStatistics, programming, domain knowledge
Work EnvironmentData preprocessing, exploratory analysisData analysis, model development, communication
Industry UsageMachine learning, data science, analyticsData science, analytics, AI projects

Principal Component Analysis (PCA) is a technique used for reducing data dimensionality by transforming variables into principal components. Data Scientists utilize PCA as part of their toolkit to simplify data and improve model performance. While PCA focuses on data transformation, Data Scientists perform broader tasks including data cleaning, modeling, and interpretation. Both roles often work together in data-driven projects within industries like tech, finance, and healthcare.

When to use PCA vs CFA?

Principal Component Analysis (PCA) is used for data reduction and identifying patterns in large datasets without predefined structures, making it suitable for exploratory analysis. Confirmatory Factor Analysis (CFA) is used to test hypotheses about the underlying structure of data and requires a theoretical model, often used in psychometrics and social sciences. As a job seeker, understanding these methods helps in roles involving data analysis, research, or statistical modeling, especially when selecting appropriate techniques for data interpretation.

Is PCA part of AI?

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction and feature extraction, often employed in machine learning and AI projects. While PCA itself is not AI, it is a common preprocessing step in AI workflows to improve model performance and reduce complexity.

What is Principal Component Analysis?

Principal Component Analysis (PCA) is a statistical technique used in data analysis and machine learning to reduce the dimensionality of large datasets. It works by transforming the original variables into a new set of uncorrelated variables called principal components, which are ordered by the amount of variance they capture from the data. PCA helps simplify data visualization, speeds up algorithms, and can improve model performance by eliminating noise and redundant features. This makes it particularly useful for exploratory data analysis and preprocessing before applying other machine learning algorithms.

How do data scientists typically collaborate with other teams when applying Principal Component Analysis (PCA) in a project?

Data scientists often work closely with domain experts, data engineers, and business analysts when using PCA in a project. They collaborate with domain experts to interpret the components and ensure the reduced dimensions still capture meaningful information for the business context. Data engineers assist in preparing and transforming the data prior to running PCA, while business analysts help communicate findings and drive decision-making based on the results. Effective communication and cross-functional teamwork are essential to ensure that PCA-driven insights are accurate, actionable, and aligned with organizational goals.

What are the real life applications of PCA?

Principal Component Analysis (PCA) is widely used in data analysis roles to reduce dimensionality and identify key features in large datasets, improving model performance and interpretability. It is applied in fields such as image compression, facial recognition, finance for risk management, and bioinformatics for gene expression analysis, often utilizing statistical software and programming languages like Python or R.

What is the difference between PCA and CNN?

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction by transforming data into principal components, often used in data preprocessing. Convolutional Neural Networks (CNNs) are deep learning models designed for image and pattern recognition tasks, involving multiple layers that learn hierarchical features. In a job context, PCA is often used for data analysis and feature extraction, while CNNs are employed in machine learning roles focused on image processing and computer vision projects.

What are the key skills and qualifications needed to thrive as a Data Scientist specializing in Principal Component Analysis (PCA), and why are they important?

To thrive as a Data Scientist specializing in PCA, you need strong statistical knowledge, experience with dimensionality reduction techniques, and a background in mathematics or data science. Proficiency in programming languages like Python or R, as well as familiarity with libraries such as scikit-learn or MATLAB, is essential for implementing PCA and analyzing large datasets. Critical thinking, problem-solving, and effective communication are valuable soft skills for interpreting results and conveying insights to stakeholders. These skills ensure accurate data analysis, meaningful interpretation, and the ability to drive data-informed decisions in complex projects.
More about Principal Component Analysis jobs
What job categories do people searching Principal Component Analysis jobs look for? The top searched job categories for Principal Component Analysis jobs are:
Principal Component Analysis jobs near you
Infographic showing various Principal Component Analysis job openings in the United States as of June 2026, with employment types broken down into 66% Full Time, 32% Part Time, 1% Temporary, and 1% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $109,393 per year, or $52.6 per hour.

SAS Analytics & Modeling- Banking

Ra

Jersey City, NJ • On-site

$100K/yr

Full-time

Posted 3 days ago

Job description

Company Description

Why we need you?


Our client is one of the unique IT Services Company serving across the globe. They are a leading operations management and analytics company that help businesses enhance growth and profitability.

We are looking for a dynamic modeling professional to join their team in Jersey City.  

Job Description

Here's what you'll be doing.

  • You'll develop Models for client as per the agreed upon schedule
  • You'll document modeling requirements based on discussions and information provided by client modeling lead/business users
  • You'll develop models based on the model requirements definition , per the agreed upon schedule with client
  • You'll develop new and enhance existing risk scorecards (application, behavior, collections/recovery etc.) or Marketing Analytics modeling (customers targeting)
  • You'll create and maintain detailed model documentation


You need these qualifications.

  • You hold a Master's degree in Statistics, Economics, Engineering, Finance, Mathematics, or a related quantitative field from tier 1 colleges
  • You have a sound Knowledge of SAS, SQL and other analytical tools (R, SPSS).
  • You've experience working in Banking, Credit Cards, Marketing Analytics, Credit Risk Modeling.
  • You've experience working with Machine Learning techniques (Support Vector Machines, Genetic Algorithms, Random Forests, K-Nearest Neighbor algorithm, Principal Component analysis etc.)
  • You've more than 3 years of experience in building models (data cleaning, dependent variable selection, independent variable study and understanding, variable reduction, bivariate analysis, variables grouping, logistic/linear model build, model validation, KS/Lift study/PSI etc.)
  • You've exposure in Online / Digital analytics & Text Mining.


Here's what we can offer.

Competitive base salary of $100K+ Bonus and Benefits+ Relocation Assistance


Work Authorization: 

US Citizens/ Green Card/ Permanent Residents/ EAD.

Additional Information

All your information will be kept confidential according to EEO guidelines. Ping me at shruthi.n at roljobs dot com to know more.

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