... linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one ...
... linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one ...
Data Scientist 3
Annapolis Junction, MD · On-site
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 3
Annapolis Junction, MD · On-site
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Manager, Biostatistics
Cambridge, MA · On-site
$106K - $176K/yr
Strong statistical knowledge through coursework or practical application in general linear models, mixed models, non-linear models, experimental design, variance components, Bayesian methods, and ...
Manager, Biostatistics
Cambridge, MA · On-site
$106K - $176K/yr
Strong statistical knowledge through coursework or practical application in general linear models, mixed models, non-linear models, experimental design, variance components, Bayesian methods, and ...
... linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one ...
... linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one ...
Data Scientist 3
Annapolis Junction, MD · On-site
$132K - $147K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 3
Annapolis Junction, MD · On-site
$132K - $147K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 3
Annapolis Junction, MD · On-site
$132K - $147K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Quick apply
Data Scientist 3
Annapolis Junction, MD · On-site
$132K - $147K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
... linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one ...
... linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one ...
Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 2
Annapolis Junction, MD · On-site
$99K - $114K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 2
Annapolis Junction, MD · On-site
$99K - $114K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Apply data science tools and various statistical techniques such as regression, generalized linear models, gradient boosting and neural networks to build, validate, and implement models for solving ...
New
Apply data science tools and various statistical techniques such as regression, generalized linear models, gradient boosting and neural networks to build, validate, and implement models for solving ...
New
Specifically, algorithms such as k-NN, Linear and Generalized Linear Models, naïve Bayes, SVM, and Random Forest. * Preferred knowledge related to how pharmaceutical companies market and sell ...
Specifically, algorithms such as k-NN, Linear and Generalized Linear Models, naïve Bayes, SVM, and Random Forest. * Preferred knowledge related to how pharmaceutical companies market and sell ...
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 2
Annapolis Junction, MD · On-site
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 2
Annapolis Junction, MD · On-site
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Quantitative Researcher
New York, NY · On-site
We don't believe in "one-size-fits-all" modeling solutions; we are open to and excited about applying all different types of statistical and machine learning techniques, from linear models to deep ...
Quantitative Researcher
New York, NY · On-site
We don't believe in "one-size-fits-all" modeling solutions; we are open to and excited about applying all different types of statistical and machine learning techniques, from linear models to deep ...
Data Scientist III with Security Clearance
Fort George G Meade, MD · On-site
$128K - $185K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist III with Security Clearance
Fort George G Meade, MD · On-site
$128K - $185K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 3
Annapolis Junction, MD · On-site
$132K - $147K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Data Scientist 3
Annapolis Junction, MD · On-site
$132K - $147K/yr
Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data ...
Linear Models information
See salary details
$52.5K - $65.6K
4% of jobs
$65.6K - $78.7K
17% of jobs
$80.6K is the 25th percentile. Wages below this are outliers.
$78.7K - $91.8K
29% of jobs
$91.8K - $104.9K
0% of jobs
$104.9K - $118K
2% of jobs
$118K - $131K
7% of jobs
$131K - $144.1K
7% of jobs
$151.1K is the 75th percentile. Wages above this are outliers.
$144.1K - $157.2K
16% of jobs
$157.2K - $170.3K
7% of jobs
$170.3K - $183.4K
5% of jobs
$183.4K - $196.5K
5% of jobs
$52.5K
$119.2K
$196.5K
How much do linear models jobs pay per year?
As of Jul 12, 2026, the average yearly pay for linear models in the United States is $119,165.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,500.00 and $152,500.00 per year, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as a Linear Models Analyst, and why are they important?
To thrive as a Linear Models Analyst, you need a strong background in statistics, mathematics, and data analysis, typically supported by a degree in a quantitative field. Proficiency with statistical software such as R, Python (with libraries like statsmodels or scikit-learn), and experience with data visualization tools are essential. Critical thinking, problem-solving, and effective communication are important soft skills for interpreting results and conveying insights to stakeholders. These skills and qualifications are crucial for accurately modeling real-world phenomena, driving data-driven decisions, and ensuring clear understanding across teams.
What are linear models?
Linear models are statistical or mathematical models that assume a linear relationship between input variables (predictors) and a single output variable (response). In these models, the effect of each predictor is additive and proportional, making them easier to interpret and analyze. Linear models are commonly used in regression analysis and can be extended to more advanced techniques like multiple linear regression and generalized linear models. They are foundational in fields like data science, economics, and engineering for making predictions and understanding relationships between variables.
What is the difference between Linear Models vs Data Analysts?
| Aspect | Linear Models | Data Analysts |
|---|---|---|
| Required credentials | Statistics, mathematics, or data science degrees; proficiency in statistical software | Statistics, data analysis, or related degrees; skills in data visualization and reporting |
| Work environment | Analytical, research-focused; often in tech, finance, or healthcare industries | Business or corporate settings; focus on interpreting data for decision-making |
| Employer usage | Developing predictive models, understanding relationships between variables | Interpreting data, creating reports, supporting business strategies |
Linear Models are statistical tools used to predict or understand relationships between variables, often requiring advanced mathematical skills. Data Analysts utilize these models among other techniques to interpret data and inform business decisions. While their roles overlap, Linear Models focus on model development, whereas Data Analysts focus on data interpretation and reporting.
What are some common challenges faced by professionals working with linear models in data analysis roles?
Professionals working with linear models often encounter challenges such as ensuring that the data meets key assumptions like linearity, independence, and homoscedasticity. Handling multicollinearity among predictors can also complicate model interpretation and accuracy. Additionally, it is crucial to balance model simplicity with predictive power, especially when dealing with large datasets or real-world, messy data. Collaborating with domain experts and data engineers is often necessary to properly preprocess data and validate model outputs.
More about Linear Models jobs
What job categories do people searching Linear Models jobs look for? The top searched job categories for Linear Models jobs are:
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Re-posted 8 days ago
Job description
What You'll be Owning: * We are actively searching for Data Scientists, located in Hawaii, to support our team. We have varying levels of Data Scientist roles, depending on years of experience and education. * Performs tasks associated with Big Data Platform management, utilizes skills in programming languages, develops prototype algorithms as well as algorithm refinements, and supports data visualization and analytics. What You Must Have : * Bachelor's Degree with 3 years of relevant experience OR Associates degree with 5 years of relevant experience * Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence). College-level requirements, or upper-level math courses designated as elementary or basic do not count. Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university. * Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python)), statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one area is strongly preferred * Active TS/SCI w/poly What Would Be Nice to Have: * Foundations: (Mathematical, Computational, Statistical) 2. Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility) * Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations) * Devise strategies for extracting meaning and value from large datasets. Make and communicate principled conclusions from data using elements of mathematics, * Statistics, computer science, and application specific knowledge. * Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in data holdings. * Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting, processing, storage and analytic capabilities and limitations.