Data Scientist - Clinical Analytics (Remote)

Job Description

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.