Nonmem Programming information
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$44.5K - $49.5K
15% of jobs
$52.1K is the 25th percentile. Wages below this are outliers.
$49.5K - $54.4K
19% of jobs
$54.4K - $59.4K
14% of jobs
The median wage is $64.3K / yr.
$59.4K - $64.3K
2% of jobs
$64.3K - $69.3K
1% of jobs
$69.3K - $74.2K
1% of jobs
$74.2K - $79.2K
13% of jobs
$79.2K - $84.1K
6% of jobs
$86.3K is the 75th percentile. Wages above this are outliers.
$84.1K - $89.1K
9% of jobs
How much do nonmem programming jobs pay per year?
As of Sep 14, 2026, the average yearly pay for nonmem programming in the United States is $70,857.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,000.00 and $88,500.00 per year, depending on experience, location, and employer.
Nonmem programming refers to the use of the NONMEM software (Nonlinear Mixed-Effects Modeling) for pharmacometric and population pharmacokinetic/pharmacodynamic analyses. NONMEM is widely used in the pharmaceutical industry to analyze clinical trial data and model how drugs are absorbed, distributed, metabolized, and excreted in different populations. Nonmem programmers write model code, manage datasets, and interpret output to support drug development and regulatory submissions. Their expertise is crucial in optimizing dosing regimens and understanding drug behaviors across various patient groups.
To thrive as a Nonmem Programmer, you need a solid background in pharmacometrics, statistics, and programming, typically with a degree in pharmacy, mathematics, or a related field. Proficiency in NONMEM software, scripting languages like R or SAS, and experience with data management systems are essential. Attention to detail, problem-solving abilities, and effective communication skills help you interpret complex data and collaborate with multidisciplinary teams. These skills ensure accurate model development, reliable data analysis, and effective support for drug development decisions.
Nonmem Programmers often encounter challenges such as managing large and complex datasets, ensuring data quality, and dealing with convergence issues during model estimation. Collaboration with pharmacometricians and statisticians is crucial, as model development is iterative and requires integrating feedback from multiple stakeholders. Additionally, maintaining compliance with regulatory requirements and thorough documentation can be demanding, but are essential for successful project delivery and regulatory submissions.
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