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Principal Component Analysis Jobs (NOW HIRING)

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

Data Scientist

Rockville, MD · On-site

$91K - $96K/yr

... Principal Component Analysis (PCA) for dimensionality reduction; use K-means clustering and Hierarchical clustering to discover conceptually meaningful classes of object; use NumPy, Pandas ...

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

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 ...

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

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

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

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 ...

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 ...

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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:
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.
Asset & Wealth Management- New York- Associate, Quantitative Internal Product Specialists- 9649762

Asset & Wealth Management- New York- Associate, Quantitative Internal Product Specialists- 9649762

Goldman Sachs, Inc.

New York, NY • On-site

$191K/yr

Other

Posted 22 days ago


Goldman Sachs rating

8.3

Company rating: 8.3 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

29th of 141 rated banks


Job description

Job Duties: Associate, Quantitative Internal Product Specialists with Goldman Sachs & Co. LLC in New York, New York. Serve as a product expert on Quantitative Investment Strategies, Alternative Investments, develop new quantitative investment ideas based on research, market structure and statistical analysis. Support existing funds and help launch new ones, supporting both internal and external clients of the Division and the Firm. Write code in Python, Java, C or SQL to solve analytical problems and produce analysis on markets and investment strategies. Create client-specific proposals and analysis tailored to address the unique needs of individual investorsDevelop content and analysis on Markets, Quantitative Investment technique. Develop performance reporting and ongoing risk contribution and performance attribution analysis across asset portfolios. Support advisors, salespeople, and clients to understand account lifecycle events. Collaborate with teams across the division to drive the commercial success of the business. Prepare methodology documentation and marketing material and complex due diligence questionnaires for the QIS funds. Develop strong working relationships across our business, working closely with portfolio managers, strategists, and engineers to gain insight into our investment process, as well as compliance, legal, controllers, operations. Collaborate with our investment team, sales and marketing to create marketing collateral and determine the best vehicle for delivery (webinar, blog posts, white paper, etc.). Serve as a point of contact for advisors and sophisticated institutional clients regarding the Firm's Alternative Investment strategies, investment processes, and performance through client meetings and call. Mentor junior members of the team and help manage and prioritize responsibilities and deliverables.

Job Requirements: Master's degree (U.S. or foreign equivalent) in Financial Engineering, Mathematics, Applied Mathematics, Computer Science, or related field and one (1) year of experience in the job offered or in a related role OR Bachelor's degree (U.S. or foreign equivalent) in Financial Engineering, Mathematics, Applied Mathematics, Computer Science, or related field and three (3) years of experience in the job offered or in a related role. Prior experience must include one (1) year of experience (with a Master's degree) or three (3) years of experience (with a Bachelor's degree) with the following: working in financial markets and investment, sales and trading teams with understanding of structured investment products, and option strategies; utilizing programming languages to perform quantitative analysis, automate reports, materials, and/or processes, specifically advanced Python or C++ experience. Development, production implementation, and maintenance of software; utilizing PowerPoint and Excel to create and update proposals and/or marketing materials; computing risk and performance analytics on structured investment products; derivatives, Option knowledge, Volatility Regimes Modeling and portfolio construction; pricing and structuring multi-asset financial derivatives. Statistical and stochastic modelling of financial products for prediction and risk management; principal component analysis, hypothesis testing, linear/non-linear optimization. Time series analysis and large dataset manipulation; and communicating complex investment concepts to non-technical audience. FINRA Series 7TO, 63 required. 10% domestic and international travel required to conduct client presentations in person. All travel costs will be reimbursed by the firm.

Salary Range: Annual base salary for this New York, New York based position is $191,922.

The Goldman Sachs Group, Inc., 2026. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.


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About Goldman Sachs

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At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869