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The R Shiny Developer Jobs (NOW HIRING)

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Data Scientist

Durham, NC · Remote

$51 - $58/hr

We are looking for candidates with strong experience in the R programming language, geospatial ... Develop and maintain interactive R Shiny dashboards for HEW datasets and scientific reporting ...

Develop data science tool with R Shiny as the web framework and deploy the artifacts into RStudio ... & DevOps functions. * 3+ years solid development experience in R, R packages, RStudio, RStudio ...

Develop data science tool with R Shiny as the web framework and deploy the artifacts into RStudio ... & DevOps functions. * 3+ years solid development experience in R, R packages, RStudio, RStudio ...

Develop data science tool with R Shiny as the web framework and deploy the artifacts into RStudio ... & DevOps functions. 3+ years solid development experience in R, R packages, RStudio, RStudio ...

Experience with the JavaScript programming language * Experience building R Shiny and Tableau dashboards * Experience using Oracle and PostgreSQL databases to retrieve, process, and store data

Experience with the JavaScript programming language * Experience building R Shiny and Tableau dashboards * Experience using Oracle and PostgreSQL databases to retrieve, process, and store data

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The R Shiny Developer information

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$13

$49

$88

How much do the r shiny developer jobs pay per hour?

As of Jun 11, 2026, the average hourly pay for the r shiny developer in the United States is $49.33, according to ZipRecruiter salary data. Most workers in this role earn between $26.44 and $67.31 per hour, depending on experience, location, and employer.

What is the difference between The R Shiny Developer vs Data Analyst?

AspectThe R Shiny DeveloperData Analyst
Required skillsProficiency in R, Shiny, data visualization, web app developmentData manipulation, statistical analysis, Excel, SQL
Work environmentDeveloping interactive web applications, coding, software projectsData interpretation, reporting, business insights
Industry usageTech, healthcare, finance, researchMarketing, finance, healthcare, consulting

The R Shiny Developer focuses on building interactive web applications using R and Shiny, often requiring coding and software development skills. Data Analysts analyze data sets to generate reports and insights, typically using statistical tools and data visualization. While both roles work with data, the R Shiny Developer emphasizes application development, whereas Data Analysts focus on data interpretation and reporting.

Infographic showing various The R Shiny Developer job openings in the United States as of June 2026, with employment types broken down into 3% As Needed, 74% Part Time, 3% Temporary, and 20% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $102,607 per year, or $49.3 per hour.

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Required Skills : Technical Skills : R Shiny ,R Studio
Domain Skills : 5. Nice to have skills : Technical Skills : PL/SQL ,3NF data modeling Domain Skills : Research and Development
Technology : Analytics
Roles & Responsibilities : As a Technical Lead, you will be responsible for leading a team of developers, designing and implementing technical solutions, conducting code reviews, and providing technical guidance and mentorship. You will collaborate with cross-functional teams to ensure the successful delivery of projects and contribute to the overall technical strategy of the organization.
Key Responsibilities:
1. R Shiny Application Development: Create and maintain interactive web applications using R Shiny, allowing end-users to visualize and interact with data in an intuitive manner.
2. Data Visualization: Collaborate with data scientists, analysts, and stakeholders to design visually compelling and user-friendly dashboards, charts, and reports.
3. Front-End Development: Implement responsive and user-friendly user interfaces using HTML, CSS, and JavaScript to enhance the user experience.
4. Data Integration: Integrate data from various sources, including databases, APIs, and flat files, ensuring data accuracy and reliability.
5. Performance Optimization: Identify and resolve performance bottlenecks in Shiny applications to ensure smooth and efficient user interactions.
6. Quality Assurance: Conduct thorough testing and debugging of Shiny applications to ensure functionality, reliability, and security.
7. Documentation: Maintain comprehensive documentation for code, applications, and processes to facilitate knowledge sharing and team collaboration.
8. Collaboration: Collaborate with cross-functional teams, including data scientists, analysts, and project managers, to understand project requirements and deliver solutions that meet business objectives.
9. Stay Current: Stay updated on the latest trends and best practices in R Shiny development and data visualization to continuously improve our applications.