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Linear Regression Jobs (NOW HIRING)

SynergisticIT

Entry Level Data Scientiest

Manchester, NH · On-site

$16.25 - $21.75/hr

Machine Learning Algorithms - Linear Regression, Logistic Regression, Decision Tree * Unsupervised/Clustering algorithms * NLP models, Deep Learning models for image classification Desired Candidate ...

Tech Group International

Lead Forecasting Engineer

Toledo, OH · On-site

$100K - $132K/yr

Familiarity with basic statistics and regression algorithms (Experience with Microsoft Linear Regression in SQL Server is a plus) * Familiarity with C# and ASP.Net is a plus * Familiarity with ...

Walmart

Manager, Advanced Analytics

San Bruno, CA

$109K - $202K/yr

Build predictive machine learning frameworks to understand customer behavior using algorithms like linear regression and logistic regression to predict future daily trends of KPIs such as GMV, CVR ...

PNC Bank

Apply statistical and mathematical techniques, including logistic and linear regression, to solve business problems. Perform data analysis, feature engineering, model training, testing, and ...

Futran Tech Solutions Pvt. Ltd.

Data Engineer

$117K - $140K/yr

GLM multiple regression, logistic regression, log-linear regression, variable selection, etc. • Comfortable working with massive unstructured data sets. • Strong SQL skills, ability to perform ...

CMP.jobs

Sr. Machine Learning Engineer, AdTech

Manhattan, NY · On-site

$61.25 - $81.25/hr

Python, Algorithms, Optimisation, NLP, Data Mining, Statistical Analysis, Neural Networks, Generalised Linear Regression, Multiclass Classification, Java, R - Advanced knowledge of Python using ...

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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Is linear regression outdated?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
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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.

Lead Forecasting Engineer

Tech Group International

Toledo, OH

$100K - $132K/yr

Full-time

Medical, Retirement, PTO

Posted 23 days ago

Job description

Technology Group International (TGI) is looking for an experienced data scientist with software development experience to join our development team. Your primary responsibility will be to lead the development of forecasting and statistical models for our enterprise application for manufacturers and distributors. You will work closely with the software, analytics, and decision systems listed in "Duties and Responsibilities." As Lead Forecasting Engineer, you will have a wealth of opportunities to contribute to the development of our enterprise application and play an integral role in the future direction of our technology stack.


Duties and Responsibilities:

  • Lead the development of various forecasting algorithms for our enterprise application
  • Generate statistical models for our enterprise application's analytics / business intelligence (BI) offering
  • Optimize and improve our enterprise application's existing analytics offering
  • Optimize and improve our enterprise application's existing forecasting and material requirements planning (MRP) system
  • Integrate new forecasting algorithms to be used throughout our software

Skills and Specifications:

  • Experience in writing SQL queries against a relational database
  • Experience in Python, R, or other Statistical Language
  • Experience with algorithms such as clustering, forecasting, anomaly detection, and neural networks
  • Experience in version control systems (preferably Git)
  • Familiarity with basic statistics and regression algorithms (Experience with Microsoft Linear Regression in SQL Server is a plus)
  • Familiarity with C# and ASP.Net is a plus
  • Familiarity with front-end technologies and frameworks such as HTML, CCS, and JavaScript

Education and Qualifications:

  • 2 - 5 years of relevant work experience
  • BS, MS, or PhD degrees in Data Science, Computer Science, Statistics, or Engineering
  • Must be a U.S. citizen
  • Written and oral communication in English required

Competitive Benefits Package:

  • Competitive compensation will be based on relevant work background
  • Health Insurance and company paid health care spending account
    401(k)
  • Holiday, personal, and vacation paid time off

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