Career Insights

Skills, strategies, and opportunities for R professionals in pharma

Overview

Based on the workshops, presentations, and industry trends from R/Pharma 2025, this guide provides actionable career advice for R professionals working in (or aspiring to work in) the pharmaceutical industry.

🔥 Skills in High Demand

1. AI/LLM Integration (🔥🔥🔥 Hot!)

Why it matters: 8+ sessions focused on AI, representing the biggest shift in pharma programming since R adoption began.

What to learn:

{ellmer} package for LLM integration
Prompt engineering basics
RAG (Retrieval-Augmented Generation) concepts
Model Context Protocol (MCP)
Privacy-preserving AI approaches

Getting started:

Workshop: Getting Started with LLM APIs
Experiment with ChatGPT/Claude API for data analysis tasks
Build a simple Shiny chatbot with {shinychat}

Career impact: Early adopters will be positioned as AI specialists in their organizations.

2. Clinical Reporting Automation

Why it matters: Industry moving from manual coding to template-based, automated TFL generation.

What to learn:

{gtsummary} and {crane} for tables
{officer} and {flextable} for Word reports
Quarto for multi-format publishing
CDISC ARS/ARM standards
{cards} for Analysis Results Data

Getting started:

Workshop: Advanced Clinical Reporting
Workshop: Cardinal TLGs
Create a personal TFL template library

Career impact: Ability to reduce TFL turnaround time from weeks to days makes you invaluable.

3. Package Development & Validation

Why it matters: Internal package development is standard practice. Validation expertise is critical for GxP environments.

What to learn:

{devtools}, {usethis}, {testthat}
Package structure and best practices
Documentation with {roxygen2}
Risk-based validation approaches
{riskmetric} for package assessment

Getting started:

Workshop: Building R Packages
Package one of your current projects
Contribute to a pharmaverse package

Career impact: Package developers and validation experts command premium salaries and have job security.

4. Shiny Application Development

Why it matters: Interactive apps for clinical data exploration, AI interfaces, and decision support are proliferating.

What to learn:

Shiny fundamentals (reactive programming)
{teal} framework for clinical apps
Shiny app validation strategies
Performance optimization
Deployment (Posit Connect)

Getting started:

Workshop: teal Framework
Build a simple adverse event explorer
Learn about Shiny app testing ({shinytest2})

Career impact: Shiny developers with validation experience are in short supply.

5. Bayesian Methods

Why it matters: FDA increasingly accepting Bayesian approaches. Powerful for small samples and adaptive designs.

What to learn:

Stan basics
{brms} for applied modeling
Bayesian workflow principles
Prior specification
MCMC diagnostics

Getting started:

Career impact: Bayesian expertise opens doors to advanced analytics roles and consulting.

6. Data Validation & Quality

Why it matters: Data quality is paramount in regulated environments. Validation is non-negotiable.

What to learn:

{pointblank} for data validation
GxP requirements (21 CFR Part 11, EU Annex 11)
IQ/OQ/PQ documentation
Automated testing strategies

Getting started:

Workshop: pointblank
Implement validation checks on your datasets
Learn about validation documentation

Career impact: Quality/validation specialists are always in demand.

🚀 Career Paths

Entry Level → Mid Career

Typical progression:

Junior Statistical Programmer (0-2 years)
- Focus: Learn CDISC standards, basic R programming
- Skills to develop: dplyr, ggplot2, SDTM/ADaM basics
- Goal: Independently create simple TFLs
Statistical Programmer (2-5 years)
- Focus: Complex TFLs, package development, validation
- Skills to develop: {gtsummary}, {admiral}, git/GitHub
- Goal: Lead TFL development for studies
Senior Statistical Programmer (5-8 years)
- Focus: Automation, AI integration, team leadership
- Skills to develop: Shiny, AI/LLM, validation strategies
- Goal: Architect solutions, mentor juniors

Specialization Paths

Path 1: AI/Data Science Specialist

Focus on LLM integration, advanced analytics, ML
Learn Python, Stan, cloud platforms (AWS)
Become the “AI expert” in your organization

Path 2: Clinical Reporting Architect

Master reporting automation (officer, Quarto, ARS)
Design template systems and workflows
Lead efficiency initiatives

Path 3: Validation & Compliance Lead

Deep dive into GxP, validation frameworks
Become risk assessment expert
Interface with regulators and auditors

Path 4: Open-Source Contributor

Contribute to pharmaverse packages
Build reputation in community
Opportunities at Posit, pharma, consulting

💼 For Experienced Professionals

Transitioning from SAS to R

Reality: SAS experience is still valuable! Many organizations need people who know both.

Strategy:

Leverage your domain knowledge
- You understand clinical trials, regulations, CDISC
- R syntax is learnable in weeks; expertise takes years
Start with parallel development
- Create R versions of your SAS programs
- Compare outputs (builds confidence)
- Document differences and learnings
Focus on R strengths
- Interactive visualizations (ggplot2, plotly)
- Shiny apps (impossible in SAS)
- Modern reporting (Quarto)
- AI integration (trivial in R, hard in SAS)
Get formal training
- GSK’s success built on structured training
- Invest in courses (R/Pharma workshops, Posit)
- Join internal R user groups

Timeline: 6-12 months to become productive; 18-24 months to become expert.

Moving into Leadership

Requirements for technical leadership:

Technical depth: Can solve complex problems
Strategic vision: Understand industry trends
Communication: Explain technical concepts to non-technical stakeholders
Mentorship: Develop junior team members
Business acumen: Connect technology to outcomes (cost savings, time reduction)

Positioning yourself:

Lead high-visibility projects
- AI pilot programs
- Automation initiatives
- Validation frameworks
Present at conferences
- R/Pharma
- PharmaSUG
- Internal symposiums
Publish and share
- Blog posts
- GitHub repositories
- White papers
Build your network
- LinkedIn presence
- Conference attendance
- Industry working groups

🎓 Learning Resources

Free Resources

Online Courses:

R for Data Science (book + online)
Statistical Rethinking (lectures on YouTube)
Posit Recipes & How-Tos

Communities:

Pharmaverse Slack
R/Pharma conference
Posit Community
Stack Overflow

Practice:

CDISC Pilot datasets
Kaggle pharma competitions
Personal projects on GitHub

Paid Resources

Courses:

Posit Academy
Statistical Rethinking course
DataCamp / Coursera specializations

Books:

R Packages (2nd edition)
Advanced R
Clinical Trial Data Analysis using R
Biostatistics textbooks

Conferences:

PharmaSUG
UseR!
rstudio::conf (now posit::conf)

🤝 Networking Strategies

Build Your Reputation

Contribute to open source
- Fix bugs in pharmaverse packages
- Add features
- Improve documentation
- Your GitHub is your portfolio
Present your work
- Internal brown bag sessions
- R/Pharma lightning talks
- Regional R user groups
Write about your experiences
- Company blog (if allowed)
- LinkedIn posts
- Medium articles
- Personal website

Engage with Community

Online:

Join pharmaverse Slack
Answer questions on Stack Overflow (r+pharma tags)
Comment on relevant LinkedIn posts
Participate in working groups (R Validation Hub, ASA-BIOP)

In-Person:

Attend R/Pharma (even virtually)
Local R user groups
Company data science meetups

🎯 Job Search Tips

For Your Resume

Highlight:

R packages you’ve developed (link to GitHub)
Automation you’ve implemented (quantify time savings)
Validation experience (GxP compliance)
AI/Shiny projects
Contributions to open-source projects

Include:

Link to GitHub profile
Link to personal website/portfolio
Specific technologies (not just “R”)

For Interviews

Prepare to discuss:complex technical problem you solved - How you approach validation - Experience with clinical data - Contributions to efficiency - Learning new technologies quickly

Ask about: - Open-source contribution policies - Training budget and opportunities - Technology stack and roadmap - Team culture around R adoption - Career development paths

Companies to Watch

Actively hiring R professionals:

Big Pharma: GSK, Roche/Genentech, Novartis, Pfizer, Moderna
Technology: Posit PBC
CROs: Many transitioning to R
Consulting: A2-AI, Jumping Rivers, Ardata
Startups: Biotech companies using modern stacks

📊 Industry Outlook

Positive Trends

✅ R adoption accelerating (GSK 50%+ is proof)
✅ AI creating new roles (LLM specialists)
✅ Regulatory acceptance (FDA neutrality)
✅ Open-source momentum (pharmaverse thriving)
✅ Remote work normalized (global opportunities)

Challenges

⚠️ Validation complexity (but solvable)
⚠️ Skills gap (more demand than supply - good for you!)
⚠️ Rapid change (must commit to continuous learning)

Bottom Line

The next 5 years will see explosive growth in demand for R professionals in pharma. Early adopters with AI/automation/validation skills will have their pick of opportunities.

💡 Final Thoughts

The R/Pharma 2025 conference made clear: this is the golden age for R professionals in pharma. With AI integration, open-source adoption, and regulatory acceptance converging, opportunities are abundant for those willing to:

Learn continuously (technology changes fast)
Contribute generously (open source is key)
Communicate effectively (technical + business)
Validate rigorously (quality is non-negotiable)
Innovate boldly (early adopters win)

Your R skills are valuable. Make them invaluable.

Stay Informed

Subscribe to pharmaverse newsletter
Follow key people on LinkedIn (Joe Zhu, Daniel Sjoberg, others from conference)
Bookmark this site and check back for updates
Join r-pharma mailing list

Career guidance compiled from R/Pharma 2025 trends and industry insights | Last updated: November 2025

--- title: "Career Insights" subtitle: "Skills, strategies, and opportunities for R professionals in pharma" toc: true --- ## Overview Based on the workshops, presentations, and industry trends from R/Pharma 2025, this guide provides actionable career advice for R professionals working in (or aspiring to work in) the pharmaceutical industry. ------------------------------------------------------------------------ ## 🔥 Skills in High Demand ### 1. AI/LLM Integration (🔥🔥🔥 Hot!) **Why it matters:** 8+ sessions focused on AI, representing the biggest shift in pharma programming since R adoption began. **What to learn:** - `{ellmer}` package for LLM integration - Prompt engineering basics - RAG (Retrieval-Augmented Generation) concepts - Model Context Protocol (MCP) - Privacy-preserving AI approaches **Getting started:** - [Workshop: Getting Started with LLM APIs](../workshops/ai-llm/getting-started-llm-apis.qmd) - Experiment with ChatGPT/Claude API for data analysis tasks - Build a simple Shiny chatbot with `{shinychat}` **Career impact:** Early adopters will be positioned as AI specialists in their organizations. ------------------------------------------------------------------------ ### 2. Clinical Reporting Automation **Why it matters:** Industry moving from manual coding to template-based, automated TFL generation. **What to learn:** - `{gtsummary}` and `{crane}` for tables - `{officer}` and `{flextable}` for Word reports - Quarto for multi-format publishing - CDISC ARS/ARM standards - `{cards}` for Analysis Results Data **Getting started:** - [Workshop: Advanced Clinical Reporting](../workshops/clinical-reporting/officer-flextable.qmd) - [Workshop: Cardinal TLGs](../workshops/clinical-reporting/cardinal-tlgs.qmd) - Create a personal TFL template library **Career impact:** Ability to reduce TFL turnaround time from weeks to days makes you invaluable. ------------------------------------------------------------------------ ### 3. Package Development & Validation **Why it matters:** Internal package development is standard practice. Validation expertise is critical for GxP environments. **What to learn:** - `{devtools}`, `{usethis}`, `{testthat}` - Package structure and best practices - Documentation with `{roxygen2}` - Risk-based validation approaches - `{riskmetric}` for package assessment **Getting started:** - [Workshop: Building R Packages](../workshops/development-validation/building-r-packages.qmd) - Package one of your current projects - Contribute to a pharmaverse package **Career impact:** Package developers and validation experts command premium salaries and have job security. ------------------------------------------------------------------------ ### 4. Shiny Application Development **Why it matters:** Interactive apps for clinical data exploration, AI interfaces, and decision support are proliferating. **What to learn:** - Shiny fundamentals (reactive programming) - `{teal}` framework for clinical apps - Shiny app validation strategies - Performance optimization - Deployment (Posit Connect) **Getting started:** - [Workshop: teal Framework](../workshops/specialized/teal-framework.qmd) - Build a simple adverse event explorer - Learn about Shiny app testing (`{shinytest2}`) **Career impact:** Shiny developers with validation experience are in short supply. ------------------------------------------------------------------------ ### 5. Bayesian Methods **Why it matters:** FDA increasingly accepting Bayesian approaches. Powerful for small samples and adaptive designs. **What to learn:** - Stan basics - `{brms}` for applied modeling - Bayesian workflow principles - Prior specification - MCMC diagnostics **Getting started:** - [Workshop: Bayesian Survival Models](../workshops/statistical-methods/bayesian-survival.qmd) - [Workshop: Debugging Stan](../workshops/statistical-methods/debugging-stan.qmd) - Statistical Rethinking book/course **Career impact:** Bayesian expertise opens doors to advanced analytics roles and consulting. ------------------------------------------------------------------------ ### 6. Data Validation & Quality **Why it matters:** Data quality is paramount in regulated environments. Validation is non-negotiable. **What to learn:** - `{pointblank}` for data validation - GxP requirements (21 CFR Part 11, EU Annex 11) - IQ/OQ/PQ documentation - Automated testing strategies **Getting started:** - [Workshop: pointblank](../workshops/clinical-reporting/pointblank.qmd) - Implement validation checks on your datasets - Learn about validation documentation **Career impact:** Quality/validation specialists are always in demand. ------------------------------------------------------------------------ ## 🚀 Career Paths ### Entry Level → Mid Career **Typical progression:** 1. **Junior Statistical Programmer** (0-2 years) - Focus: Learn CDISC standards, basic R programming - Skills to develop: dplyr, ggplot2, SDTM/ADaM basics - Goal: Independently create simple TFLs 2. **Statistical Programmer** (2-5 years) - Focus: Complex TFLs, package development, validation - Skills to develop: {gtsummary}, {admiral}, git/GitHub - Goal: Lead TFL development for studies 3. **Senior Statistical Programmer** (5-8 years) - Focus: Automation, AI integration, team leadership - Skills to develop: Shiny, AI/LLM, validation strategies - Goal: Architect solutions, mentor juniors ### Specialization Paths #### Path 1: AI/Data Science Specialist - Focus on LLM integration, advanced analytics, ML - Learn Python, Stan, cloud platforms (AWS) - Become the "AI expert" in your organization #### Path 2: Clinical Reporting Architect - Master reporting automation (officer, Quarto, ARS) - Design template systems and workflows - Lead efficiency initiatives #### Path 3: Validation & Compliance Lead - Deep dive into GxP, validation frameworks - Become risk assessment expert - Interface with regulators and auditors #### Path 4: Open-Source Contributor - Contribute to pharmaverse packages - Build reputation in community - Opportunities at Posit, pharma, consulting ------------------------------------------------------------------------ ## 💼 For Experienced Professionals ### Transitioning from SAS to R **Reality:** SAS experience is still valuable! Many organizations need people who know both. **Strategy:** 1. **Leverage your domain knowledge** - You understand clinical trials, regulations, CDISC - R syntax is learnable in weeks; expertise takes years 2. **Start with parallel development** - Create R versions of your SAS programs - Compare outputs (builds confidence) - Document differences and learnings 3. **Focus on R strengths** - Interactive visualizations (ggplot2, plotly) - Shiny apps (impossible in SAS) - Modern reporting (Quarto) - AI integration (trivial in R, hard in SAS) 4. **Get formal training** - GSK's success built on structured training - Invest in courses (R/Pharma workshops, Posit) - Join internal R user groups **Timeline:** 6-12 months to become productive; 18-24 months to become expert. ------------------------------------------------------------------------ ### Moving into Leadership **Requirements for technical leadership:** - **Technical depth:** Can solve complex problems - **Strategic vision:** Understand industry trends - **Communication:** Explain technical concepts to non-technical stakeholders - **Mentorship:** Develop junior team members - **Business acumen:** Connect technology to outcomes (cost savings, time reduction) **Positioning yourself:** 1. **Lead high-visibility projects** - AI pilot programs - Automation initiatives - Validation frameworks 2. **Present at conferences** - R/Pharma - PharmaSUG - Internal symposiums 3. **Publish and share** - Blog posts - GitHub repositories - White papers 4. **Build your network** - LinkedIn presence - Conference attendance - Industry working groups ------------------------------------------------------------------------ ## 🎓 Learning Resources ### Free Resources **Online Courses:** - R for Data Science (book + online) - Statistical Rethinking (lectures on YouTube) - Posit Recipes & How-Tos **Communities:** - Pharmaverse Slack - R/Pharma conference - Posit Community - Stack Overflow **Practice:** - CDISC Pilot datasets - Kaggle pharma competitions - Personal projects on GitHub ### Paid Resources **Courses:** - Posit Academy - Statistical Rethinking course - DataCamp / Coursera specializations **Books:** - R Packages (2nd edition) - Advanced R - Clinical Trial Data Analysis using R - Biostatistics textbooks **Conferences:** - PharmaSUG - UseR! - rstudio::conf (now posit::conf) ------------------------------------------------------------------------ ## 🤝 Networking Strategies ### Build Your Reputation 1. **Contribute to open source** - Fix bugs in pharmaverse packages - Add features - Improve documentation - Your GitHub is your portfolio 2. **Present your work** - Internal brown bag sessions - R/Pharma lightning talks - Regional R user groups 3. **Write about your experiences** - Company blog (if allowed) - LinkedIn posts - Medium articles - Personal website ### Engage with Community **Online:** - Join pharmaverse Slack - Answer questions on Stack Overflow (r+pharma tags) - Comment on relevant LinkedIn posts - Participate in working groups (R Validation Hub, ASA-BIOP) **In-Person:** - Attend R/Pharma (even virtually) - Local R user groups - Company data science meetups ------------------------------------------------------------------------ ## 🎯 Job Search Tips ### For Your Resume **Highlight:** - R packages you've developed (link to GitHub) - Automation you've implemented (quantify time savings) - Validation experience (GxP compliance) - AI/Shiny projects - Contributions to open-source projects **Include:** - Link to GitHub profile - Link to personal website/portfolio - Specific technologies (not just "R") ### For Interviews **Prepare to discuss:**complex technical problem you solved - How you approach validation - Experience with clinical data - Contributions to efficiency - Learning new technologies quickly **Ask about:** - Open-source contribution policies - Training budget and opportunities - Technology stack and roadmap - Team culture around R adoption - Career development paths ### Companies to Watch **Actively hiring R professionals:** - **Big Pharma:** GSK, Roche/Genentech, Novartis, Pfizer, Moderna - **Technology:** Posit PBC - **CROs:** Many transitioning to R - **Consulting:** A2-AI, Jumping Rivers, Ardata - **Startups:** Biotech companies using modern stacks ------------------------------------------------------------------------ ## 📊 Industry Outlook ### Positive Trends ✅ **R adoption accelerating** (GSK 50%+ is proof)\ ✅ **AI creating new roles** (LLM specialists)\ ✅ **Regulatory acceptance** (FDA neutrality)\ ✅ **Open-source momentum** (pharmaverse thriving)\ ✅ **Remote work normalized** (global opportunities) ### Challenges ⚠️ **Validation complexity** (but solvable)\ ⚠️ **Skills gap** (more demand than supply - good for you!)\ ⚠️ **Rapid change** (must commit to continuous learning) ### Bottom Line **The next 5 years will see explosive growth in demand for R professionals in pharma.** Early adopters with AI/automation/validation skills will have their pick of opportunities. ------------------------------------------------------------------------ ## 💡 Final Thoughts The R/Pharma 2025 conference made clear: **this is the golden age for R professionals in pharma**. With AI integration, open-source adoption, and regulatory acceptance converging, opportunities are abundant for those willing to: - **Learn continuously** (technology changes fast) - **Contribute generously** (open source is key) - **Communicate effectively** (technical + business) - **Validate rigorously** (quality is non-negotiable) - **Innovate boldly** (early adopters win) **Your R skills are valuable. Make them invaluable.** ------------------------------------------------------------------------ ::: callout-tip ## Stay Informed - Subscribe to pharmaverse newsletter - Follow key people on LinkedIn (Joe Zhu, Daniel Sjoberg, others from conference) - Bookmark this site and check back for updates - Join r-pharma mailing list ::: ------------------------------------------------------------------------ *Career guidance compiled from R/Pharma 2025 trends and industry insights \| Last updated: November 2025*