Senior R Programmer - Causal Inference & Targeted Learning
Join our cutting-edge startup revolutionizing decision-making through advanced statistical methods
Location: Remote (US time zones)
Job Type: Part-time or Full-time Contract (minimum 20 hours/week)
Reports To: Founding Team
About Us
TL Revolution is an early-stage startup revolutionizing causal inference and targeted learning founded by leading experts in the field. We develop cutting-edge statistical software that empowers businesses, regulators, and policymakers with smarter, evidence-based decision-making.
Our team combines decades of experience in biostatistics and machine learning, with seed funding secured to build our initial product suite. We're currently partnering with multi-national pharmaceutical companies and collaborating with regulators to enhance clinical study analyses.
This is an exciting opportunity to be part of an early-stage startup where your work will have a direct impact on business strategy and product development. If you're passionate about causal inference and statistical programming, this is a rare opportunity to work alongside leading experts in the field.
Role Overview
We're seeking a Senior R Programmer to co-lead our development alongside our founders. This is a newly created role as we scale our technical capabilities. The ideal candidate will bring:
- Deep expertise in R, R Shiny, causal inference, and statistical modeling
- Experience in pharmaceutical research applications
- Strong foundation in statistics and machine learning
- Collaborative mindset to work with our research team in building scalable, reproducible, and deployable statistical solutions
Key Responsibilities
We have two distinct focus areas - please indicate in your application which track(s) you're interested in:
Track 1: Shiny Application Development
- Design and develop interactive Shiny applications for visualizing and implementing causal inference methods (40%)
- Create intuitive user interface that makes complex statistical concepts accessible to non-technical users (20%)
- Implement responsive dashboards using R Shiny, ggplot2, R Markdown, and Quarto (20%)
- Optimize application performance and ensure scalability for enterprise use (10%)
- Collaborate with UX designers and stakeholders to refine application requirements (10%)
- Integrate applications with existing R packages
- Ensure applications meet regulatory compliance requirements for clinical decision-making
Track 2: R Package Development
- Architect and develop a suite of R packages implementing targeted learning methodology (40%)
- Design robust APIs for causal inference techniques, especially TMLE and Super Learning ensemble methods (20%)
- Create comprehensive unit tests, documentation, and vignettes following CRAN standards (15%)
- Optimize code for computational efficiency and scalability with large datasets (15%)
- Collaborate with academic researchers to implement cutting-edge statistical methods (10%)
- Ensure package integration with broader R ecosystem
- Maintain version compatibility and dependency management
Required Qualifications
- Master's or Ph.D. in Computational Biostatistics, Statistics, Computer Science, or related field (or equivalent practical experience)
- Expertise in R, R Shiny, and production-level R package development (5+ years experience)
- Strong knowledge of causal inference and machine learning in pharma/biotech/healthcare
- Experience developing scalable, reproducible data pipelines with version control
- Proficiency with R package development, testing, and documentation
- Excellent problem-solving and communication skills for collaborating with technical & non-technical stakeholders
Preferred Qualifications
- Experience with machine learning techniques for causal inference, including Super Learning
- Pharma/biotech experience, including clinical trials, observational studies, and real-world studies
- Familiarity with Python, R APIs, RESTful interfaces, and dashboarding tools
- Experience with Git, CI/CD pipelines, and collaborative development environments
- Experience with cloud environments (AWS, GCP, Azure)
- Experience with regulatory submissions or FDA-compliant statistical implementations
- Leadership experience in a startup or fast-paced research setting
What We Offer
- Competitive location-based salary with potential equity participation
- SF Bay Area, NYC, Boston, Seattle: $150K - $200K annually (pro-rated for part-time)
- Other US locations: $100K - $150K annually (pro-rated for part-time)
- Flexible remote work and a collaborative startup culture where your contributions matter
- Opportunity to work alongside globally recognized leaders in causal inference and targeted learning
- Modern tech stack including R, Python, and cloud-based deployment environments
- A chance to pioneer advanced statistical methods in pharmaceutical research with global impact
How to Apply
Email recruiting@TLrevolution.com with:
- Cover letter (≤ 2 pages) highlighting your experience with causal inference methods
- Resume/CV
- Link to GitHub repository or CRAN package showcasing your work, particularly any relevant to causal inference or targeted learning
- Brief description of a causal inference challenge you've solved (optional)
- Please specify which track you're applying for (Track 1, Track 2, or both)
- Current location (for compensation consideration)
Applications will be reviewed on a rolling basis. First-round interviews will begin within four weeks of receiving your application. The entire process typically takes 6-8 weeks.
Visit www.tlrevolution.com to learn more about our team and mission.
Note: Principals only—no agencies, SEO firms, or content mills.
