ZipRecruiter

Log In

1

Linear Regression Jobs (NOW HIRING)

Sonsoft Inc

Big Data Architect

Collierville, TN

$56.50 - $72.50/hr

Hands on experience in different machine learning algorithms, like logistic regression, linear regression, random forest, Neural Networks, Time Series. * Ability to present results of statistical ...

AVEVA DRUG DELIVERY SYSTEMS INC

QC Analyst III

Miramar, FL

$22 - $29.50/hr

Ability to apply advances mathematical concepts such as exponents, logarithms, quadratic equations, linear regression, and permutations. Ability to apply mathematical operations to such tasks as ...

Arsenault Inc

Data Analyst

Houston, TX

Experience implementing one or several of the following statistical techniques: linear regression, time series analysis, experimental design, hypothesis testing, and A/B testing * Experience with ...

EVgo

Data Scientist

Preferred : • Proficient in scripting with SQL and Python • Applies analytic techniques for data exploration, data visualization, linear regression, categorical models and machine learning. • ...

next page

Showing results 1-20

All JobsLinear Regression Jobs

Linear Regression information

See salary details

$11

$51

$69

How much do linear regression jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for linear regression in the United States is $51.44, according to ZipRecruiter salary data. Most workers in this role earn between $42.55 and $59.38 per hour, depending on experience, location, and employer.

Is linear regression outdated?

Linear regression remains a fundamental statistical method used in data analysis and machine learning jobs. While newer models like decision trees and neural networks are popular for complex tasks, linear regression is still valuable for simple, interpretable predictions and baseline modeling. Proficiency in statistical tools like R or Python's scikit-learn is often required for these roles.

What are the key skills and qualifications needed to thrive as a Data Analyst specializing in Linear Regression, and why are they important?

To excel as a Data Analyst specializing in Linear Regression, you need a strong background in statistics, mathematics, and data interpretation, typically supported by a relevant degree in data science, mathematics, or a related field. Familiarity with statistical software and programming languages such as R, Python (with libraries like scikit-learn), and data visualization tools is essential. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting results and conveying insights to stakeholders. These competencies are crucial for accurately modeling relationships within data, making data-driven decisions, and providing actionable recommendations.

What is linear regression?

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. It aims to find the best-fitting straight line (in simple linear regression) that predicts the value of the dependent variable based on the values of the independent variables. Linear regression is widely used in data analysis, forecasting, and machine learning for tasks such as predicting trends and estimating relationships between variables. It is one of the simplest and most interpretable techniques in statistics and predictive modeling.

What jobs pay $250 an hour?

In roles related to data analysis and machine learning, highly experienced data scientists or quantitative analysts using skills like linear regression can earn $250 an hour or more, especially as consultants or contractors. Such high rates are typically associated with specialized expertise, advanced degrees, and extensive industry experience, often in consulting firms or freelance work. These positions often require strong statistical knowledge, programming skills, and a proven track record of delivering value to clients.

What jobs use linear regression?

Jobs such as data analyst, data scientist, machine learning engineer, and quantitative researcher frequently use linear regression to analyze data, build predictive models, and inform decision-making. Proficiency in statistical tools like R or Python and understanding of data modeling are essential skills for these roles.

What are common challenges faced by professionals working with linear regression models in real-world data analysis?

One of the main challenges when applying linear regression in real-world scenarios is dealing with data that may not meet the assumptions of linearity, homoscedasticity, and normality of residuals. Additionally, real data often contains outliers or multicollinearity among predictors, which can skew results and reduce model reliability. Professionals typically spend significant time on data preprocessing, feature selection, and validating model performance. Collaboration with domain experts is crucial to ensure that chosen variables make sense and that model outputs are actionable for business or research decisions.

What is the difference between Linear Regression vs Data Analyst?

AspectLinear RegressionData Analyst
Primary RoleBuilds statistical models to predict outcomesAnalyzes data to identify trends and support decision-making
Skills RequiredStatistics, programming (Python/R), data modelingData visualization, Excel, SQL, basic statistics
Work EnvironmentData science teams, research projectsBusiness units, reporting, dashboards
CertificationsData Science, Machine Learning certificationsData Analysis, Business Intelligence certifications

While Linear Regression focuses on creating predictive models using statistical techniques, Data Analysts interpret data to generate insights and support business decisions. Both roles require analytical skills, but Linear Regression specialists often have a stronger background in statistics and programming, whereas Data Analysts focus on data visualization and reporting.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist or working in roles like linear regression analysis. Many professionals successfully transition into data science later in their careers by acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications. Experience, continuous learning, and practical skills are more important than age in this field.
More about Linear Regression jobs
What states have the most Linear Regression jobs? States with the most job openings for Linear Regression jobs include:
What job categories do people searching Linear Regression jobs look for? The top searched job categories for Linear Regression jobs are:
Linear Regression jobs near you
Infographic showing various Linear Regression job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 26% Part Time, and 1% Contract. Highlights an 70% Physical, 3% Hybrid, and 27% Remote job distribution, with an average salary of $106,997 per year, or $51.4 per hour.
Asset & Wealth Management-New York-Associate, Quantitative Engineering-9225677

Asset & Wealth Management-New York-Associate, Quantitative Engineering-9225677

Goldman Sachs, Inc.

New York, NY • On-site

$113K - $189K/yr

Full-time

Posted 16 days ago

Goldman Sachs rating

8.2

Company rating: 8.2 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

40th of 145 rated banks

Job description


Job Duties: Associate, Quantitative Engineering with Goldman Sachs & Co. LLC in New York, New York. Develop, implement, and document scenarios comprised of a broad range of economic and financial variables for businesses within the Firm. Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues. Analyze large data sets (structured and unstructured) to build predictive models of business-relevant market variables. Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming. Build and challenge risk models, identify and quantify vulnerabilities across market, credit, liquidity risk and modeling. Create and maintain clear and complete technical documentation of the risk-model performance testing approach and process.
Job Requirements: Master's degree (U.S. or foreign equivalent) in Computer Science, Financial Engineering, Applied Mathematics, Data Science, Physics, Operations Research or related quantitative field and one (1) year of experience in job offered or a related quantitative engineering role OR Bachelor's degree (U.S. or foreign equivalent) in Computer Science, Financial Engineering, Applied Mathematics, Data Science, Physics, Operations Research or related quantitative field and two (2) years of experience in job offered or a related quantitative engineering role. Prior experience must include one (1) year of experience (with a Master's degree) or two (2) years of experience (with a Bachelor's degree) with 5 of the 7 following skills: C++, Java, or Python; developing probability and pricing models utilizing financial mathematics principles, including stochastic calculus, no-arbitrage pricing theory, partial differential equations, multivariable calculus, linear algebra, numerical methods, optimization, probability, or random processes; quantitative analysis and model development using advanced econometric, statistical, and mathematical techniques, including Bayesian analysis, time series analysis, or machine learning algorithms; performing risk management or scenario-based analysis; developing quantitative risk analytics, including factor models; developing rigorous and scalable data management and analysis tools to provide risk oversight and support the investment process; and statistics and data driven performance analysis, including Linear Regression or Time Series Analysis to measure performance.
Salary Range: Annual base salary for this New York, New York-based position is $113,000 - $189,000.
©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.

What Goldman Sachs employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Goldman Sachs logo

About Goldman Sachs

Sourced by ZipRecruiter

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

View All Goldman Sachs Jobs

Apply