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Job Description Contact: Neisha Camacho/Terra Parsons - teamnt@penfieldsearch.com No 3rd party candidates This is a hands-on role at the intersection of Data Science, Biostatistics, Statistical Programming, and Clinical Data Management. The ideal candidate combines strong programming and technical development skills with an understanding of clinical studies and the ability to build practical solutions that improve how teams' access, analyze, visualize, and work with clinical data. This individual will support ongoing studies while also helping build the technical infrastructure, automation, and reusable tools that enable a growing Biometrics organization to work more efficiently

The successful candidate will be forward-thinking, collaborative, and comfortable bringing new ideas and modern approaches to a highly cross-functional environment. Primary Responsibilities Develop data science solutions and analytical tools to support clinical studies and broader Biometrics initiatives. Design and build dashboards and data visualizations that enable effective clinical data review and decision-making.

Develop reusable tools, workflows, and infrastructure that can be leveraged by Statistical Programming, Biostatistics, Data Management, and other team members. Build and maintain automated workflows using GitHub and GitHub Actions. Create automated quality checks and validation processes to improve code quality and reliability.

Develop workflows that support code review, testing, and deployment of analytical applications and tools. Work with databases and data sources to support data integration, access, analysis, and visualization. Apply R and SAS to clinical data and analytical challenges, selecting the appropriate technology based on the specific use case.

Utilize Python when it provides the most effective solution to a particular technical or analytical problem. Support the development and maintenance of interactive analytical applications using R Shiny. Partner closely with Biostatistics, Statistical Programming, Data Management, and other cross-functional stakeholders to understand study needs and develop effective technical solutions.

Identify opportunities to automate manual processes and introduce more efficient and innovative ways of working. Establish and promote effective Git/GitHub workflows, version control, and collaborative development practices. Help less experienced team members adopt modern programming, version control, automation, and application development practices.

Contribute ideas and technical expertise as the organization's Data Science and clinical analytics capabilities continue to grow. Qualifications Bachelor's or Master's degree in Data Science, Statistics, Biostatistics, Computer Science, or a related quantitative field. Significant experience working in Data Science, Statistical Programming, Clinical Analytics, or a related technical function within the pharmaceutical, biotechnology, or clinical research environment.

Advanced programming experience in R and SAS. Strong hands-on experience with Git and GitHub. Demonstrated experience creating and using GitHub Actions to automate testing, validation, or other development workflows.

Experience developing analytical applications, dashboards, and data visualizations, including R Shiny. Experience working with databases and integrating data into analytical workflows. Strong understanding of software development and collaborative coding practices, including version control, code review, testing, and automation.

Ability to develop reusable technical solutions and infrastructure that can be leveraged by other team members. Experience working with clinical study data and supporting study teams. Strong cross-functional communication skills with the ability to work effectively across Biostatistics, Statistical Programming, Data Management, and other clinical functions.

Ability to operate independently in a small, growing organization while remaining highly collaborative. Preferred Experience Working knowledge of Python and the ability to apply it selectively when it is the appropriate solution. Experience building infrastructure, frameworks, or reusable tools that enable other programmers, statisticians, or data scientists to work more efficiently.

Experience automating development and quality-control processes. Experience helping teams adopt Git/GitHub, R Shiny, automation, or other modern development practices. Key Success Factors The successful candidate will be: Hands-on and technically strong.

Able to personally build solutions rather than only direct the work of others. Forward-thinking. Brings ideas and looks for better, more efficient ways of solving problems.

Practical. Selects technology based on the problem rather than forcing every problem into the same technical solution. Collaborative.

Works effectively across clinical and technical functions and communicates well with colleagues with varying levels of technical expertise. A builder. Comfortable joining a growing organization and helping establish the tools, infrastructure, and ways of working that will support the team as it scales.

Self-directed. Able to identify needs, propose solutions, and drive work forward while collaborating closely with the broader team.