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Clinical R Programmer Jobs in Oregon (NOW HIRING)

Sr. Scientist

OR · On-site +1

Proficiency in programming languages such as Python or R, SQL, and data visualization tools ... teams, including clinicians, scientists, biostatisticians, regulatory affairs, and external ...

New

$83K/yr

Advanced skill in the use of programming languages such as SAS, SQL, R, or ST ATA. Verbal and ... Clinical) or 4 years of work experience above the minimum qualification * 5 Years of Experience ...

OR

$160K - $180K/yr

... clinicians, health economics and outcomes researchers (HEOR), engineers, and digital health ... Proficiency with R, Python (scikit-learn, pandas, numpy), and SQL * Experience working cross ...

You'll work alongside nurses, clinical staff, and operational leaders to analyze healthcare data ... Experience with Python, R, or other data analysis languages is a plus * Prior exposure to ...

OR

$290K/yr

About Spyre Therapeutics Spyre Therapeutics is a clinical-stage biotechnology company pioneering ... Lead the biometrics team including statistician, data manager, and statistical programmer at a ...

Independently design, engineer, and implement new, actuarially sound methodologies for measuring ... Utilize strong matrix influence to build consensus among clinical, operational, and finance leaders

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Showing results 1-20

Clinical R Programmer information

What are the key skills and qualifications needed to thrive as a Clinical R Programmer, and why are they important?

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 Clinical R Programmers?

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 are some 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 Oregon? For Clinical R Programmer jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Clinical R Programmer jobs in Oregon look for? The top searched job categories for Clinical R Programmer jobs in Oregon are:
What cities in Oregon are hiring for Clinical R Programmer jobs? Cities in Oregon with the most Clinical R Programmer job openings:
Infographic showing various Clinical R Programmer job openings in Oregon as of July 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 100% In-person job distribution.
Sr. Scientist

Sr. Scientist

Natera

OR • On-site, Remote

Other

Posted 2 days ago

New


Natera rating

7.7

Company rating: 7.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

50th of 103 rated laboratories


Job description

Sr. Scientist Real-World Evidence - Chronic Kidney Disease and Rare Disease

Location: San Carlos, CA, Austin, TX, or Remote, USA

Sr. Scientist -CKD and rare disease

Job Summary

Natera is seeking an innovative and driven bioinformatics scientist to lead and execute cutting-

edge "real-world evidence" (RWE) analyses and predictive modeling across key areas of Organ

Health, Rare Disease, and Women's Health. This unique role requires a blend of expertise in

bioinformatics, strong project management skills, and a dedication to impactful data

visualization to advance our understanding and application of genomics in a real-world clinical

setting.Key Responsibilities

RWE and Bioinformatics Analysis: Lead large-scale genomics data analysis,

specifically in Chronic Kidney Disease (CKD), to extract actionable insights. Apply

advanced bioinformatics tools and techniques to interpret genomic data within the

context of RWE studies.

Predictive Analytics & Modeling: Develop and implement robust predictive models to

forecast clinical trends and outcomes using RWE and genomics data. Utilize machine

learning and statistical modeling to uncover patterns that inform clinical decision-making

and strategic business development.

Data Visualization and Communication: Create and implement innovative data

visualization strategies to effectively communicate complex genomic analysis results.

Utilize tools like R, R Shiny, or Python libraries (e.g., Matplotlib, Seaborn) to build

intuitive, interactive, and impactful visual representations.

Project Leadership: Own and manage genomics projects from initial concept through to

final delivery, ensuring all initiatives are completed efficiently (on time and within budget)

while maintaining the highest quality and scientific standards.

Cross-Functional Partnership: Collaborate closely with Sales, R&D, Data Science,

Business Development, Medical Affairs, Product Management, and Engineering to

seamlessly integrate genomics data into broader research and development initiatives.

Data Stewardship: Facilitate the integration of genomics data with diverse data types

(e.g., clinical and demographic) to enrich analyses. Oversee the management of large

datasets, ensuring data integrity, security, and confidentiality.

Reporting and Publication: Prepare detailed reports and manuscripts for publication.

Present complex genomics data and analyses in a clear, concise manner to varied

audiences, including technical experts and non-technical stakeholders.

Innovation and Development: Maintain current knowledge of the latest developments

in genomics and bioinformatics. Propose and develop novel methods and technologies

for advanced data analysis and predictive modeling.

Stakeholder Engagement: Act as a key liaison between the technical team and non-

technical partners, engaging with stakeholders to define project goals, communicate

progress, and discuss findings that drive the business forward.

Qualifications

Ph.D. in Bioinformatics, Computational Biology, Genetics, or a closely related field.

A minimum of 5 years of post-doctoral or professional experience in a relevant field.

Proven expertise in bioinformatics, with a strong emphasis on genomics data analysis.

Extensive experience managing and analyzing large-scale genomic and healthcare

datasets.

Demonstrated expertise in human genomics, including familiarity with inherited

disorders, genomic alterations, molecular mechanisms, and disease biology.

Expert knowledge of bioinformatics tools for data processing, including mapping, variant

calling, CNV analysis, and core statistical methods.

Solid understanding of real-world data (RWD) sources such as electronic health records,

claims data, patient registries, or health surveys. Ability to interpret clinical endpoints,

understand patient cohorts, and successfully collaborate with clinical stakeholders.

Proficiency in predictive analytics, machine learning, and statistical modeling is required.

Excellent project management skills with a proven record of leading successful, complex

projects.

Proficiency in programming languages such as Python or R, SQL, and data visualization

tools.

Exceptional written and verbal communication skills for both technical and non-technical

audiences.

Proven experience collaborating effectively with diverse cross-functional teams,

including clinicians, scientists, biostatisticians, regulatory affairs, and external

stakeholders.

Experience managing one or more direct or indirect reports is a plus.

Knowledge of translational medicine and/or early discovery in the biotech or

pharmaceutical industry is a plus.

Personal Attributes

Ability to produce high-quality written documentation for varying audiences.

Demonstrated capacity to work independently while effectively managing multiple

objectives and timelines.

A desire to work in a fast-paced environment with the potential for high impact as part of

a small, dynamic team.

Additional expertise in germline genetics, particularly in relation to organ health and

women's health, is a significant advantage.


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