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Tidyverse Jobs in Raleigh, NC (NOW HIRING)

Must have advanced R programming skills, including tidyverse, ggplot2, Markdown, Quarto, Shiny, etc. Python and/or SAS programming skills are a plus, but not required. * Knowledge of CDISC standards ...

Must have advanced R programming skills, including tidyverse, ggplot2, Markdown, Quarto, Shiny, etc. Python and/or SAS programming skills are a plus, but not required. * Knowledge of CDISC standards ...

Principal R Programmer

Durham, NC · On-site

$98K - $273K/yr

Must have advanced R programming skills, including tidyverse, ggplot2, Markdown, Quarto, Shiny, etc. Python and/or SAS programming skills are a plus, but not required. * Knowledge of CDISC standards ...

Must have advanced R programming skills, including tidyverse, ggplot2, Markdown, Quarto, Shiny, etc. Python and/or SAS programming skills are a plus, but not required. * Knowledge of CDISC standards ...

Tidyverse information

What are Tidyverse packages?

The Tidyverse is a collection of R packages designed for data science. These packages share an underlying design philosophy, grammar, and data structures, making it easy to manipulate, explore, and visualize data. The core Tidyverse packages include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, and forcats, among others. They help streamline common data analysis tasks and are widely used by R programmers for efficient and readable code.

What are the key skills and qualifications needed to thrive as a Tidyverse Data Analyst, and why are they important?

To thrive as a Tidyverse Data Analyst, you need strong data manipulation, visualization, and statistical analysis skills, typically supported by a degree in statistics, data science, or a related field. Proficiency in R programming and mastery of Tidyverse packages (such as dplyr, ggplot2, tidyr, and readr) are essential, along with knowledge of version control systems like Git. Analytical thinking, attention to detail, and clear communication are standout soft skills in this role. These skills ensure accurate data insights, reproducible workflows, and effective collaboration with stakeholders for data-driven decision-making.

How does working as a Tidyverse data analyst typically involve collaboration with other teams or departments?

As a Tidyverse data analyst, collaboration is a core aspect of the role. You'll often work closely with stakeholders from various departments, such as marketing, finance, or product teams, to understand their data needs and translate them into actionable insights using R and the Tidyverse package suite. Regular communication is essential for gathering requirements, presenting findings, and ensuring that analyses align with business goals. Additionally, you may partner with data engineers or IT to access and manage datasets, and with other analysts to share best practices and streamline workflows.

What is the difference between Tidyverse vs Data Analyst?

AspectTidyverseData Analyst
Primary FocusData manipulation, visualization, and analysis using R packagesInterpreting data, creating reports, and supporting decision-making
Skills & ToolsR programming, ggplot2, dplyr, tidyr, readrExcel, SQL, statistical analysis, data visualization tools
Work EnvironmentData science teams, research labs, analytics departmentsBusiness, finance, marketing, healthcare sectors
Required CredentialsKnowledge of R, data analysis, statisticsDegree in statistics, data science, or related fields

While Tidyverse refers to a collection of R packages for data manipulation and visualization, Data Analysts utilize these tools along with other skills to interpret data and generate insights. Tidyverse is a technical toolkit, whereas Data Analyst is a role that applies these tools in various industries to support decision-making.

Principal R Programmer

Principal R Programmer

IQVIA Holdings

Durham, NC • On-site

Other

Posted 3 days ago


Job description

R Programmer Position

Join a sponsor-dedicated team, progressing with in-house study activities over the years. Experienced R programmer needed to provide technical expertise for clinical PK/PD department to meet internal and external needs.

Uses R and companion software to develop custom programming code to generate summary tables, data listings, graphs and derived datasets as specified in the statistical analysis plan and programming specifications. Works to ensure that outputs meet quality standards and project requirements.

Summary of the Essential Functions of the Job:

  • Data preparation and cleaning:
    • Cleaning and transforming raw clinical trial data from various sources to ensure accuracy and consistency for PK/PD analysis.
  • Programming PK/PD analyses:
    • Writing R or Rmarkdown code to perform descriptive analysis of PK/PD data and statistical analysis of exposure-response relationships.
  • Data visualization:
    • Creating clear and informative graphs and tables to effectively communicate PK/PD findings.
  • CDISC compliance:
    • Ensuring data is formatted according to CDISC standards for regulatory submissions
  • Validation and quality control:
    • Performing thorough validation checks on programming code and analysis results to maintain data integrity.
  • Identifies problems and develops tools that increase the efficiency and capacity of the Clinical PK/PD Programming group.

Minimum Requirements

  • Bachelor's degree in Math, Stats, Computer Science or similar
  • 5+ years of industrial experience
  • Must have advanced R programming skills, including tidyverse, ggplot2, Markdown, Quarto, Shiny, etc. Python and/or SAS programming skills are a plus, but not required.
  • Knowledge of CDISC standards (SEND, SDTM, and ADaM) is required.
  • Desire to work in clinical PK/PD – knowledge and experience of PK/PD concepts and related programming is highly desirable.
  • Experience visualizing/presenting data for internal stakeholders or clients
  • Capable of implementing more advanced modeling and statistical procedures as requested by study team.
  • Strong understanding of clinical trial data and extremely hands on in data manipulations, analysis, and reporting/modeling of analysis results. Including handling of data issues and uncleaned data.
  • Professional attitude, self-motivated, logical thinking
  • Excellent attention to detail
  • Strong organization skills and ability to work on multiple tasks simultaneously while achieving quality standards and meeting deadlines
  • Good verbal and written communication skills. Strong interpersonal skills and ability to work collaboratively across teams
  • Ability to problem solve and develop innovative approaches along with a drive to learn and master new techniques and technologies