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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 · On-site

$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 ...

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. • ...

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 ...

Apple

Senior Robotics Algorithm Engineer

Cupertino, CA · On-site

$128K - $177K/yr

... Linear Regression, Decision Trees, etc.) Preferred Qualifications Strong proficiency with classical robotic algorithms (optimization, graph search, etc.) Strong applied math background (e.g ...

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How much do linear regression jobs pay per hour?

As of Aug 6, 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.

What are the key skills and qualifications needed to thrive as a data analyst specializing in linear regression?

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 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.

More about Linear Regression jobs
What states have the most Linear Regression jobs? States with the most job openings for Linear Regression jobs include:
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Linear Regression jobs near you
Infographic showing various Linear Regression job openings in the United States as of August 2026, with employment types broken down into 75% Full Time, 8% Temporary, and 17% Contract. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $106,997 per year, or $51.4 per hour.

Sr. Machine Learning Engineer, AdTech

CMP.jobs

Manhattan, NY

$61.25 - $81.25/hr

Full-time

Re-posted 12 days ago

Job description

Sr. Machine Learning Engineer, AdTech As a member of our Data Science Engineering team, the Sr. Machine Learning Engineer, AdTech will focus on optimizing real-time bidding strategies and auction mechanics to efficiently spend ad budgets and deliver against campaign targets. In addition to the above, you will work with the greater Data Science/Engineering teams on: - Analyzing and optimizing real-time bidding strategies and online auction mechanics; - Developing new or improving existing models of event predictions; - New feature engineering for multiple machine learning models: - User embeddings and clustering; fraud detection, etc. - Cross-device user identification, cookieless mechanisms development; - Mining different data sources; - Supporting existing codebase for data integration and production support for our core models. Requirements: 5 years minimum of experience in machine learning/data science Key Skills: Python, Algorithms, Optimisation, NLP, Data Mining, Statistical Analysis, Neural Networks, Generalised Linear Regression, Multiclass Classification, Java, R - Advanced knowledge of Python using standard DS packages (numpy/pandas/scikit, etc.); Being able to optimize and speed-up code. - 3+ years of RTB Auction or similar online technologies. In addition to the above, you’ll need to have strong knowledge in the following areas: - Algorithms and Data Structures (e.g., sorting, search tree, binary heap, trie; time & mem complexities of algorithms) - Probability and Statistics (e.g., hypothesis testing; Markov process and its stationary distributions, stochastic matrix and its properties; Bayesian inference) - ML & DS (e.g., dimensionality reduction, geometry of PCA / SVD and of L1 / L2 regularisation, Decision trees and their ensembles, collaborative filtering, Thompson sampling / MCMC, Neural Networks, etc.)

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