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Principal Component Analysis Jobs (NOW HIRING)

Principal Component Engineer Company: The Boeing Company Boeing Defense, Space & Security (BDS) is ... Conducts analysis to determine performance or reasons for failures of EEE parts or processes

Principal Component Engineer Company: The Boeing Company Boeing Defense, Space & Security (BDS) is ... Conducts analysis to determine performance or reasons for failures of EEE parts or processes

We are looking to add a Principal Component Engineer I to our team. If you enjoy working in a ... Perform bill-of-materials cost analysis, components research and obsolescence mitigation, and ...

We are looking to add a Principal Component Engineer I to our team. If you enjoy working in a ... Perform bill-of-materials cost analysis, components research and obsolescence mitigation, and ...

Principal Component Engineer Company: The Boeing Company Boeing Defense, Space & Security (BDS) is ... Conducts analysis to determine performance or reasons for failures of EEE parts or processes

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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.
Principal Component Engineer

Principal Component Engineer

Boeing

El Segundo, CA

$176K - $239K/yr

Full-time

Medical, Life, Retirement

Posted 28 days ago


Boeing rating

8.5

Company rating: 8.5 out of 10

Based on 592 frontline employees who took The Breakroom Quiz

32nd of 518 rated manufacturers


Job description

Principal Component Engineer

Company:

The Boeing Company

Boeing Defense, Space & Security (BDS) is seeking a high-performing, experienced, self-motivated Component Engineer with Electro-Optics/Infrared/Photonics devices to provide support for a wide variety of space programs at Boeing in El Segundo, California.

Position Responsibilities:

  • Collects information to support definition of requirements for active/photonic EEE parts, materials and processes used in the manufacture and engineering design of satellite and aerospace products

  • Incorporates changes to specifications and other documents to manage the deployment of materials, parts and processes

  • Performs tests to qualify new EEE parts and processes to meet requirements

  • Assists with activities to qualify suppliers to applicable requirements

  • Conducts analysis to determine performance or reasons for failures of EEE parts or processes

  • Assists in implementing corrective and preventive actions

  • Develops production systems to satisfy user requirements

  • Uses project management tools to meet cost, technical, and schedule requirements

  • Systems engineering experience in electro-optical hardware development

  • Excellent communications skills both verbal and written

  • Demonstrated ability to drive closure and resolve complex technical issues/challenges

  • A variety of customer experience and exposure in markets such as intelligence, defense, science, and commercial

  • Experience developing materials for and presented at preliminary and critical design reviews

  • Experience in developing high reliability components or subsystems for space including writing qualification plans and executing environmental tests

  • Hands-on experience in delivering EO/photonics hardware/products

  • Experience developing materials for and presented at preliminary and critical design reviews

  • Incorporates environmental health and safety, LEAN and Quality principles into (1) materials and processes, (2) research, design and qualification, (3) work procedures and (4) labs and office areas

Successfully completed a Tier 5 Investigation (T5), formerly known as a Single Scope Background Investigation (SSBI) or have you been enrolled in a Continuous Vetting program such that periodic updates are no longer required

Basic Qualifications:

  • Bachelor of Science degree in Engineering, Computer Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement

  • 1+ years' experience in the design and development of EO/IR sensor system

  • 1+ years' experience laser and electro-optical systems and optical communication systems requirements development, integration, testing and analysis

  • Military standards and specifications for design, test and documentation

Typical Education

Level 5: Education/experience typically acquired through advanced technical education from an accredited course of study in engineering, engineering technology (includes manufacturing engineering technology), computer science, engineering data science, mathematics, physics or chemistry (e.g. Bachelor) and typically 14 or more years' related work experience or an equivalent combination of technical education and experience or non-US equivalent qualifications. In the USA, ABET accreditation is the preferred, although not required, accreditation standard.

Relocation:

Relocation assistance is not a negotiable benefit for this position. Candidates must live in the immediate area or relocate at their own expense.

Referral Bonus:

Referral to this role is eligible for bonus

Drug Free Workplace:

Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies.

Shift Work Statement:

This position is for 1st shift

At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities.

The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work.

The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements.

Pay is based upon candidate experience and qualifications, as well as market and business considerations.

Typical Summary Pay Range:

Level 5: $176,800- $239,200

Language Requirements:

Not Applicable

Education:

Bachelor's Degree or Equivalent

Relocation:

Relocation assistance is not a negotiable benefit for this position.

Export Control Requirement:

This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee.

Safety Sensitive:

This is not a Safety Sensitive Position.

Security Clearance:

This position requires a current Tier 5 (T5), formerly known as a Single Scope Background Investigation (SSBI) (U.S. Citizenship required) or requires candidate agreed to enter a Continuous Evaluation program.

Visa Sponsorship:

Employer will not sponsor applicants for employment visa status.

Contingent Upon Award Program

This position is not contingent upon program award

Shift:

Shift 1 (United States of America)

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Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.

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