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Linear Models Jobs (NOW HIRING)

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 ...

... 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 ...

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 ...

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 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 ...

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Linear Models information

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$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?

AspectLinear ModelsData Analysts
Required credentialsStatistics, mathematics, or data science degrees; proficiency in statistical softwareStatistics, data analysis, or related degrees; skills in data visualization and reporting
Work environmentAnalytical, research-focused; often in tech, finance, or healthcare industriesBusiness or corporate settings; focus on interpreting data for decision-making
Employer usageDeveloping predictive models, understanding relationships between variablesInterpreting 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
Infographic showing various Linear Models 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 $119,165 per year, or $57.3 per hour.

Data Scientist 3

Gormat

Annapolis Junction, MD

Full-time

Re-posted 11 days ago


Job description

We are seeking a Data Scientist proficient in Python and Jupyter Notebook to support a Natural Language Processing (NLP) project. You will help to accurately and automatically tokenize language data with spoken or written origins, develop automated solutions for the annotation of language data with parts of speech information, and improve existing models by scoring performance against human-generated annotations for speech and text.

The Level 3 Data Scientist shall possess the following capabilities:

  • Foundations: (Mathematical, Computational, Statistical).
  • Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility).
  • Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations).
  • Ability to make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge.
  • Ability to use 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 feature and limitations inherent in Government 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.
  • DS position in X32 as a support for a Natural Language Processing (NLP) project to accurately and automatically tokenize language data with spoken or written origins; develop automated solutions for the annotation of language data with parts of speech information, and improved existing models by scoring performance against human-generated annotations for speech and text.

Qualifications:

  • Bachelor's Degree with 10 years of relevant experience, associate's degree with 12 years of experience may be considered for individuals with in-depth experience that is clearly related to the position.
  • Bachelor'sDegree 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 requirement, or upper-level math courses designated as elementary or basic do not count.
  • 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 on 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.

TS/SCI with polygraph is required.