Data Scientist Overview • The Data Scientist role combines programming, biostatistics, and clinical data management expertise to develop innovative solutions for clinical analytics. • Collaborate with cross-functional teams to create tools for efficient data access, analysis, and visualization. • Support ongoing clinical studies while building reusable technical infrastructure for the Biometrics organization. • Utilize R, SAS, and Python to address complex analytical challenges and develop interactive applications. • Promote modern programming practices, automation, and collaborative development workflows within the organization. • Contribute to the growth of the organization's clinical analytics capabilities through innovation and technical expertise. • Work remotely as a contractor, providing flexibility and autonomy in project execution. Data Scientist Key Responsibilities & Duties • Develop data science solutions and analytical tools for clinical studies and Biometrics initiatives. • Design and implement dashboards and visualizations for effective clinical data review and decision-making. • Create reusable tools and workflows to enhance team efficiency in data management and analysis. • Build and maintain automated workflows using GitHub and GitHub Actions for quality assurance. • Develop interactive analytical applications using R Shiny to support clinical analytics. • Collaborate with Biostatistics, Statistical Programming, and Data Management teams to understand study needs. • Identify opportunities for automation and introduce innovative solutions to improve processes. • Mentor team members in adopting modern programming and application development practices. Data Scientist Job Requirements • Bachelor's or Master's degree in Data Science, Statistics, Biostatistics, or related field. • Minimum of 4 years of experience in Data Science, Statistical Programming, or Clinical Analytics. • Proficiency in R, SAS, and Python for data analysis and application development. • Experience with Git, GitHub, and GitHub Actions for collaborative development workflows. • Strong skills in developing dashboards, visualizations, and analytical applications using R Shiny. • Understanding of clinical study data and ability to support study teams effectively. • Ability to operate independently while collaborating in a cross-functional environment. • Preferred experience in automating development and quality-control processes.