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Clinical R Programmer Jobs in Maryland (NOW HIRING)

GLOTECH, Inc

Biostatisticians

Rockville, MD

... clinical trials, and epidemiological studies that explore health processes; examines the impact of ... Develops code in SAS, R, MPlus, SPSS & Stata * Provides analytic programming support for all phases ...

GLOTECH, Inc

Biostatisticians

Rockville, MD · On-site

... clinical trials, and epidemiological studies that explore health processes; examines the impact of ... Develops code in SAS, R, MPlus, SPSS & Stata * Provides analytic programming support for all phases ...

micro1 AI

Director of Biostatistics

Baltimore, MD · Remote

$60 - $100/hr

Advanced proficiency in R, Python, SAS, or Stata applied to real clinical or epidemiological datasets. * Strong attention to methodological detail, clinical interpretation, and regulatory sensitivity.

State of Maryland - MDH Potomac Center

MD · On-site

... and programming.\r\n Matching newly-acquired skills to enhance growth and development of persons ... clinical practicum in a developmental disability or\r\nsimilar behavior modification setting for ...

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Clinical R Programmer information

What is a Clinical R programmer?

Clinical R Programmers are professionals who use the R programming language to manage, analyze, and visualize clinical trial data in the pharmaceutical, biotech, or healthcare industries. They play a key role in preparing statistical reports, generating tables, listings, and figures (TLFs), and ensuring data integrity for regulatory submissions. Clinical R Programmers collaborate with statisticians, data managers, and clinical teams to ensure the accuracy and compliance of clinical trial results with industry standards and regulatory requirements.

What skills and qualifications are needed to be a Clinical R programmer?

To thrive as a Clinical R Programmer, you need a solid background in statistics, R programming, and clinical trial data analysis, often supported by a degree in statistics, biostatistics, or a related field. Expertise in SAS, CDISC standards (SDTM/ADaM), and familiarity with clinical data management systems are commonly required. Attention to detail, problem-solving skills, and effective communication enable you to interpret data accurately and collaborate with cross-functional teams. These skills are vital for ensuring reliable statistical outputs that support regulatory submissions and data-driven decisions in clinical research.

What are common challenges faced by Clinical R programmers when working with clinical trial data?

Clinical R Programmers often encounter challenges such as handling large and complex datasets, ensuring strict compliance with regulatory standards (like CDISC SDTM and ADaM), and maintaining data integrity throughout the analysis process. Collaboration can be demanding, as programmers must frequently coordinate with biostatisticians, data managers, and clinical teams to interpret data requirements and resolve discrepancies. Staying updated with evolving industry guidelines and managing tight project timelines are also common aspects of the role.

What is the difference between Clinical R Programmer vs Clinical SAS Programmer?

AspectClinical R ProgrammerClinical SAS Programmer
Required CredentialsTypically requires a degree in statistics, biostatistics, or related field; proficiency in R programmingUsually requires a degree in statistics, biostatistics, or related field; proficiency in SAS programming
Work EnvironmentOften works in research-focused settings, academia, or biotech companies using open-source toolsCommonly employed in pharmaceutical companies, CROs, and clinical trial data analysis using SAS
Industry UsageGrowing in popularity for data analysis and visualization in clinical researchStandard in clinical trial data management and regulatory submissions

While both roles involve programming for clinical data analysis, Clinical R Programmers focus on using R for statistical analysis and visualization, whereas Clinical SAS Programmers primarily use SAS for data management and reporting. The choice depends on the company's preferred tools and project requirements.

What are popular job titles related to Clinical R Programmer jobs in Maryland?

For Clinical R Programmer jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Clinical R Programmer jobs in Maryland look for?

The top searched job categories for Clinical R Programmer jobs in Maryland are:

What cities in Maryland are hiring for Clinical R Programmer jobs?

Cities in Maryland with the most Clinical R Programmer job openings:

Clinical R Programmer jobs near you
Infographic showing various Clinical R Programmer job openings in Maryland as of August 2026, with employment types broken down into 82% Full Time, and 18% Part Time. Highlights an 88% In-person, 6% Hybrid, and 6% Remote job distribution.

Biostatisticians

GLOTECH, Inc

Rockville, MD

Full-time

Re-posted 16 days ago

Job description

We seek Biostatisticians to join our team in support of our civilian federal government client who conducts and supports laboratory research, clinical trials, and epidemiological studies that explore health processes; examines the impact of disability and disease; and sponsors training programs for scientists, health care providers, and researchers .

The position is located in Bethesda, MD.

The position performs the following work responsibilities on a regular basis:

  • Formulates and investigates theoretical statistical techniques & models for Senior Scientists
  • Conducts statistical analyses
  • Develops code in SAS, R, MPlus, SPSS & Stata
  • Provides analytic programming support for all phases of DB development.
  • Implements statistical methods established, evaluates results, investigates anomalous findings, & implements resolutions to correct aberrant data.
  • Presents analyses findings.
  • Co-authors papers.

Qualification Requirements: Master’s Degree in Statistics or related field and 2+ years of directly related experience.  Experience to include using R, SAS, Excel, Latex, statistical method development, statistical modeling, longitudinal data analysis, & latent variable modeling.

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