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Remote R Shiny Developer Jobs in Connecticut (NOW HIRING)

Aequor

This opportunity can be remote. Duties and responsibilities: • Support Oncology drug development ... Skills: • Programming experience with Python and R for bioinformatic data analysis in unix-like ...

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Remote R Shiny Developer information

What is a remote R Shiny developer?

A Remote R Shiny Developer is a software professional who specializes in building interactive web applications using the R programming language and the Shiny package, while working from a location outside of a traditional office setting. These developers are responsible for designing, coding, and maintaining Shiny apps that allow users to visualize and analyze data interactively. They often collaborate with data scientists, analysts, and business stakeholders to deliver solutions for data-driven decision making. Remote R Shiny Developers need strong skills in R programming, web development, and communication tools to work effectively in distributed teams.

What are the key skills and qualifications needed to thrive as a remote R Shiny developer?

To thrive as a Remote R Shiny Developer, you need strong proficiency in R programming, data visualization, and experience building interactive web applications with Shiny, often supported by a degree in computer science, statistics, or a related field. Familiarity with version control systems like Git, deployment tools such as Shiny Server or Docker, and knowledge of SQL databases are commonly required. Excellent problem-solving, communication, and self-motivation skills help you collaborate effectively in remote teams and deliver user-focused solutions. These skills ensure robust, maintainable applications and smooth teamwork in a distributed work environment.

What are some common challenges faced by remote R Shiny developers when collaborating with distributed teams?

Remote R Shiny Developers often encounter challenges such as coordinating across different time zones, ensuring clear communication regarding project requirements, and maintaining code consistency within a collaborative environment. To address these, teams typically rely on regular virtual meetings, shared documentation, and version control systems like Git. Building strong communication skills and being proactive in providing updates can help streamline teamwork and reduce misunderstandings, ultimately leading to more efficient project delivery.

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

AspectRemote R Shiny DeveloperData Analyst
Required SkillsProficiency in R, Shiny, data visualization, web app developmentData manipulation, statistical analysis, Excel, SQL
Work EnvironmentRemote, project-based, software development teamsRemote or on-site, business intelligence teams
Industry UsageTech, healthcare, finance, researchFinance, marketing, healthcare, consulting

Remote R Shiny Developers focus on building interactive web applications using R and Shiny, often working in software or tech environments. Data Analysts analyze data sets to generate insights, typically using statistical tools and visualization software. While both roles may work remotely and require analytical skills, R Shiny Developers specialize in app development, whereas Data Analysts focus on data interpretation and reporting.

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Remote R Shiny Developer jobs near you

Data Scientist - Clinical Analytics (Remote)

Fairfield, CT • On-site, Remote

Penfield Search Partners
Recruiting and Staffing Services • 11 - 50 employees
Learn more about Penfield Search Partnersexternal

Full-time

Posted 12 days ago

Job description

Contact: Neisha Camacho/Terra Parsons –
No 3rd party candidates

This hands-on role sits at the intersection of Data Science, Biostatistics, Statistical Programming, and Clinical Data Management. The ideal candidate combines strong programming skills with clinical study experience and can build practical solutions that improve how teams access, analyze, visualize, and work with clinical data.

This individual will support ongoing studies while building technical infrastructure, automation, and reusable tools for a growing Biometrics organization. The successful candidate will be forward-thinking, collaborative, and comfortable introducing modern approaches in a cross-functional environment.

Primary Responsibilities

  • Develop data science solutions, analytical tools, dashboards, and visualizations to support clinical studies and data review.
  • Build reusable tools, workflows, and infrastructure for Statistical Programming, Biostatistics, Data Management, and other teams.
  • Build automated workflows using GitHub/GitHub Actions for quality checks, validation, code review, testing, and deployment.
  • Work with databases and data sources to support integration, analysis, and visualization.
  • Apply R and SAS to clinical data and analytical challenges and use Python when appropriate.
  • Develop and maintain interactive applications using R Shiny.
  • Partner across Biometrics and other clinical functions to understand study needs and develop effective technical solutions.
  • Identify opportunities to automate manual processes and improve efficiency.
  • Establish effective Git/GitHub, version control, and collaborative development practices.
  • Help team members adopt modern programming, automation, and application development practices.
  • Contribute technical expertise as Data Science and clinical analytics capabilities grow.

Qualifications

  • Bachelor's or Master's degree in Data Science, Statistics, Biostatistics, Computer Science, or a related quantitative field.
  • Significant Data Science, Statistical Programming, Clinical Analytics, or related experience within pharma, biotech, or clinical research.
  • Advanced programming experience in R and SAS.
  • Experience building and maintaining R packages.
  • Strong hands-on experience with Git/GitHub and GitHub Actions.
  • Experience developing analytical applications, dashboards, and visualizations, including R Shiny.
  • Experience with databases and integrating data into analytical workflows.
  • Strong understanding of version control, code review, testing, and automation.
  • Experience building reusable technical solutions and infrastructure.
  • Experience working with clinical study data and supporting study teams.
  • Strong cross-functional communication skills.
  • Ability to work independently in a small, growing organization while remaining highly collaborative.

Preferred Experience

  • Working knowledge of Python.
  • Experience building infrastructure, frameworks, or reusable tools for programmers, statisticians, or data scientists.
  • Experience automating development and quality-control processes.
  • Experience helping teams adopt Git/GitHub, R Shiny, automation, or other modern development practices.

Key Success Factors

  • Hands-on: Personally builds solutions rather than only directing others.
  • Forward-thinking: Brings ideas and seeks more efficient ways to solve problems.
  • Practical: Selects technology based on the problem.
  • Collaborative: Works effectively across clinical and technical functions.
  • Builder: Comfortable establishing tools, infrastructure, and new ways of working.
  • Self-directed: Identifies needs, proposes solutions, and drives work forward.