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Statistical Modeling Jobs in Washington (NOW HIRING)

Design, develop, and execute test cases for statistical modeling and analytical software applications. * Collaborate closely with quantitative engineers to investigate and resolve test case failures.

Design and implement statistical models, predictive analytics, forecasting methods, optimization models, and decision-support tools. * Extract, transform, integrate, and analyze structured, semi ...

Formulates and investigates theoretical statistical techniques & models for Senior Scientists * Conducts statistical analyses * Develops code in SAS, R, MPlus, SPSS & Stata * Provides analytic ...

Develop and implement statistical models, predictive analytics, and machine learning algorithms. * Design experiments and evaluate models to ensure accuracy, reliability, and scalability. * Translate ...

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Statistical Modeling information

See Washington salary details

$41.3K

$62.7K

$112.1K

How much do statistical modeling jobs pay per year?

As of Jul 13, 2026, the average yearly pay for statistical modeling in Washington is $62,689.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,600.00 and $68,000.00 per year, depending on experience, location, and employer.

How much do data modelers make?

Data modelers, also known as statistical modelers, typically earn between $70,000 and $120,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in machine learning or programming may earn higher salaries, especially in tech hubs or large organizations.

Are statisticians highly paid?

Statisticians are generally well-paid, with median salaries often above the national average, especially for those with advanced skills in statistical software, programming, and data analysis. Salaries vary based on experience, education, industry, and location, but the profession is considered financially rewarding compared to many other roles in data analysis and modeling.

What are some common challenges faced by professionals in statistical modeling roles, and how can they be managed?

Professionals in statistical modeling often encounter challenges such as dealing with incomplete or messy data, selecting the most appropriate modeling techniques, and clearly communicating complex results to non-technical stakeholders. Managing these challenges typically involves collaborating closely with data engineers and domain experts, employing robust data cleaning practices, and staying up-to-date with new statistical methods. Additionally, effective communication skills are essential for translating technical findings into actionable business insights, ensuring that modeling efforts drive real-world impact.

What do statistical models do?

Statistical modeling involves creating mathematical representations of data to analyze relationships, make predictions, and inform decision-making. Professionals in this field use tools like regression analysis and statistical software to interpret complex data sets and support evidence-based conclusions.

What is statistical modeling?

Statistical modeling is the process of using mathematical models and statistical techniques to analyze data, identify patterns, and make predictions or inferences. It involves building models that represent relationships between variables in real-world systems. These models can be used for forecasting, hypothesis testing, and decision-making in various fields such as business, science, and engineering. Statistical modeling helps turn raw data into actionable insights by quantifying uncertainty and highlighting significant trends.

What are the key skills and qualifications needed to thrive as a Statistical Modeler, and why are they important?

To excel as a Statistical Modeler, a solid background in statistics, mathematics, and data analysis—often supported by a degree in a quantitative field—is essential. Proficiency with statistical software such as R, Python, SAS, or SPSS and familiarity with data visualization tools are typically required. Strong problem-solving skills, critical thinking, and effective communication help convey complex findings to non-technical stakeholders. These skills ensure accurate model development, actionable insights, and effective decision-making based on data.

What is the difference between Statistical Modeling vs Data Analyst?

AspectStatistical ModelingData Analyst
Required CredentialsDegree in statistics, mathematics, or related field; proficiency in statistical softwareDegree in data science, statistics, or related; strong analytical skills
Work EnvironmentResearch, academia, or data-driven industries; focus on model developmentBusiness, marketing, or finance; focus on data interpretation and reporting
Employer & Industry UsageUsed in industries requiring predictive models and complex analysisUsed across various industries for data reporting and insights

Statistical Modeling involves creating mathematical models to understand data patterns and make predictions, often requiring advanced statistical knowledge. Data Analysts focus on interpreting data, generating reports, and providing actionable insights. While both roles work with data, Statistical Modeling emphasizes model development, whereas Data Analysts concentrate on data interpretation and presentation.

Is AI replacing statisticians?

Statisticians play a key role in designing experiments, analyzing data, and interpreting results, and AI tools are used to enhance these tasks rather than replace the profession. AI can automate routine data processing, but statisticians are needed for complex modeling, decision-making, and ensuring data quality. Proficiency in statistical software and programming languages like R or Python remains essential for the job.
What cities in Washington are hiring for Statistical Modeling jobs? Cities in Washington with the most Statistical Modeling job openings:
Infographic showing various Statistical Modeling job openings in Washington as of July 2026, with employment types broken down into 82% Full Time, 15% Part Time, 1% Temporary, and 2% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution, with an average salary of $62,689 per year, or $30.1 per hour.
2026 PhD Graduate - Statistics and Data Science

2026 PhD Graduate - Statistics and Data Science

The Johns Hopkins University Applied Physics Laboratory

Laurel, MD • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired today. Applications are no longer accepted.


Job description

Description

Do you enjoy exploring and analyzing data to find data-driven solution to complex problems?

Do you want to contribute to work that is crucial to maintaining our national security and strength?

Are you continuously searching for new ways to grow your knowledge and improve your skills?

If you are graduating with a PhD in Statistics, Physics, Mathematics, Computer Science, or a related field, we would love to have you join our team! We are seeking a new PhD graduate with expertise in statistics to support multi-disciplinary teams performing a variety of quantitative tasks for defense and national security applications. You will be joining a varied team of engineers, software developers, statisticians, data scientists, and analysts who are committed to advancing the state-of-the-art in performance evaluation of the nation's strategic weapons systems throughout their lifecycle. We believe in continually growing our capabilities and cultivating a work environment that embraces innovation, integrity, trust, and teamwork.

As a member of our team, you will...

  • Work with multi-disciplinary teams to support development of data collection, processing, and analysis efforts to assess the performance of a number of systems supporting the Navy and Air Force.
  • Develop and evaluate statistical models for complex defense applications, including uncertainty quantification, inference, forecasting, and decision support.
  • Apply statistical reasoning for data-driven studies, selecting appropriate methods (e.g., Bayesian or frequentist approaches) based on the problem.
  • Quantify and communicate uncertainty, assumptions, and limitations to support sound decision-making in complex, real-world setting.
  • Use internal funding opportunities to shape the direction of future research.
  • Communicate technical knowledge by articulating ideas clearly through papers and presentations to technical staff, management, and government decision makers.

Qualifications

You meet our minimum qualifications for the job if you...

  • Have a PhD in Data Science, Statistics, Physics, Mathematics, Computer Science or a related field.
  • Demonstrate strong interpersonal skills and the ability to work independently and on a team.
  • Have strong foundations in statistical inference, probability, statistical modeling, and experimental design.
  • Have experience using scientific programming tools such as Python, R, or similar languages for quantitative analysis.
  • Have experience investigating and adapting modern statistical and computational methods to address emerging analysis challenges in sparse, noisy, or high-dimensional data setting.
  • Demonstrate experience with statistical modeling, inference, or experimental design approaches such as Bayesian methods, regression, causal inference, or hypothesis testing...
  • Are able to obtain Interim Secret level security clearance by your start date and can ultimately obtain Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

You will go above and beyond our minimum requirements if you...

  • Have experience in project management or leading technical teams.
  • Have experience in writing technical proposals, particularly to government research projects.
  • Have experience mentoring students, teaching, or communicating complex technical concepts in academic, research, or professional settings.
  • Have contributed to peer-reviewed publications, technical reports, or presentations in statistics, machine learning, applied mathematics, or related fields.
  • Have experience using probabilistic programming frameworks such as Stan, PyMC, or similar tools.
  • Have research or professional experience developing reproducible analytical workflows, computational research tools, or statistical methodologies for complex quantitative problems.

About Us

Why Work at APL?

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities athttps://www.jhuapl.edu/careers.

All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law.APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contactAccessibility@jhuapl.edu.

The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.


Minimum Rate
$105,000 Annually
Maximum Rate
$245,000 Annually