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Principal Component Analysis Jobs (NOW HIRING)

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 ...

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 ...

Workila

Modellor

Jersey City, NJ · On-site

... 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 ...

Prescient Edge

SME- Data Scientist

Augusta, 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 ...

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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 Jul 15, 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.

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 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 July 2026, with employment types broken down into 87% Full Time, 11% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $109,393 per year, or $52.6 per hour.

Product Analytics Manager

RathRecruiting

San Jose, CA

Full-time

Re-posted 11 days ago

Job description

Company Description

About the company

We're working with the market-leading cloud solution for authenticating digital personas and transactions on the Internet. These guys are verifying more than 20 billion annual transactions supporting 30,000 websites and 4,500 customers globally. Their flagship solution is deployed across a variety of industries, including financial services, e-commerce, payments and lending, media, government and insurance.

Job Description

Description

  • Use data from the largest real-time fraud detection platform on the planet to craft solutions with data analysis, statistical modeling, and supervised machine learning. This role has the potential to cause the most immediate real-world impact in the form of increased profits, lower authentication friction, and reduced credential abuse.You'll leverage a real-time platform analyzing billions of transactions per month for our Fortune 500 customers in Financial Services, Insurance, e-Commerce, and On-Demand Services. These tools will allow you to attain a unique perspective of the Internet and every persona connected to it.
  • You'll be continually collaborating with internal services and engineering teams, customer-facing account teams, and external business leaders and risk managers. The data driven prototypes, reports and models you build will go head-to-head against some of the most motivated attackers in the world to protect billions in revenue.
Qualifications

Responsibilities

  • Direct and execute multiple medium to large analytic projects along with rest of the stakeholders in the services, engineering & products team
  • Build prototypes to demonstrate innovative data driven product ideas
  • Assist in monitoring and maintaining global fraud detection policies, models & reports
  • Collaborate with the internal teams to fully understand business requirements and desired business outcomes
  • Contribute to the development of the team's analytical skills and business knowledge
  • Be the face of data in the organization and promote


Data/Technical qualifications

  • 3+ years of analytical experience and graduate degree in Quantitative field such as Statistics, Mathematics, Computer Science, Economics, or equivalent experience preferred
  • Demonstrated hands on expertise with Hadoop (Imapala/Hive)/SQL & Python
  • Proficiency in some of the following statistical techniques: Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Neural Networks, Clustering, Principal Component Analysis, Factor analysis, etc
  • Demonstrated experience in planning, organizing, and managing multiple analytic projects with diverse cross-functional stakeholders



Additional Information

All your information will be kept confidential according to EEO guidelines.

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