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Principal Component Analysis Jobs (NOW HIRING)

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 ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · On-site

Dimension Reduction techniques (Principal Component analysis, Singular Value Decomposition etc.) * Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.) * Experience ...

Transunion

Senior Data Analyst

Chicago, IL

$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 ...

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 ...

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 ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

Dimension Reduction techniques (Principal Component analysis, Singular Value Decomposition etc.) * Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.) * Experience ...

Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

Dimension Reduction techniques (Principal Component analysis, Singular Value Decomposition etc.) * Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.) * Experience ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

Dimension Reduction techniques (Principal Component analysis, Singular Value Decomposition etc.) * Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.) * Experience ...

Trans Union LLC

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 ...

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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All JobsPrincipal Component Analysis Jobs

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.
Data Science Specialist

Data Science Specialist

Adidev Technologies Inc

Manhattan, NY • Remote

Full-time

Posted 24 days ago

Job description

Adidev Technologies Inc 

www.adidevtechnologies.com

URGENT HIRE - HIRING PROCESS - 24-48 HOURS!

Adidev Technologies is seeking 1-2 yrs of relevant experience in Data Science. A project can last anywhere from 6 months to 18 months. Salary varies depending on experience, and we are in search of candidates looking to start as soon as possible. Excellent written and oral communication are required as is the ability to work well in a team environment.

If you are looking for a new challenge and are ready to make an impact on a growing team, then this will be a perfect fit. As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and debugging large-scale applications for one of our well-known clients.

Adidev Technologies is a growing software consulting company that is constantly expanding. As we are working with renowned clients and ready to take on new ones, we are seeking brilliant software engineers. Not only do we offer a great team to work with, but we also offer you an opportunity to make an immediate impact and get rewarded accordingly

 

Job Description

  • Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation systems, environmental systems, and/or agronomic problems.
  • Strong foundation in Python programming in a cloud environment.
  • Strong quantitative abilities, distinctive problem-solving, and excellent analysis skills
  • Expertise in data wrangling using SQL,
  • Practical knowledge and experience with cloud-computing systems and platforms, including the routine deployment of pipelines through Kubernetes
  • Fluency in querying/extracting/aggregating data via SQL scripting.
  • Extract, load and transform data (ETL) from structured and unstructured sources
  • Apply Natural Language Processing and Computer Vision to solve business use cases,
  • Strong skills in scientific data analyses, modeling, visualization and communication of results.
  • Knowledge of Python libraries (NumPy, Pandas, SciKit-Learn, TensorFlow, PyTorch), Spacy, MongoDB, PostgreSQL, Flask, streamlet and a good knowledge of data pipelines construction
  • Ph.D., M.S. or B.S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote Sensing Science, Environmental Sciences, Computational Astronomy or related scientific discipline


Must  have 

  • Understanding of various machine learning algorithms (e.g. SVM, Random Forests, Gradient Boosting, Log-Log regression, XGBoost, Lasso, Ridge, Clustering techniques, Neural Networks and others)
  • Regression (e.g. ? Linear/Logistic/MNL/Mixed Effects/Regularization)
  • Classification (K-means, Hierarchical, Latent Class, DBScan, SVM)
  • Dimension Reduction techniques (Principal Component analysis, Singular Value Decomposition etc.)
  • Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.)
  • Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras
  • Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest
  • Distributed computing tools and cloud technology (AWS)

QUALIFICATIONS

  • Degree in Data Science, Computer Science, Engineering, Math, or Statistics preferred

  • At least 2 yrs of relevant experience in Data Science


SKILLS

  • SQL, statistical modeling, Feature engineering, Data visualization, Deploying models to production, Python programming, AWS, Domains(Healthcare/ Manufacturing/ Marketing/ Financial/ Telecommunication), powerbi/tableau, data warehouse

Benefits

  • Competitive Salary

  • Paid Relocation

  • Remote Support

  • Guaranteed Regular Salary Reviews

  • Job Type: W2 or Contract 1099 (full-time - 40 hours)

Employment Type: FULL_TIME

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