Data & Quantitative Sciences Statistical Programming Intern - Summer 2027

Position Summary

The Statistical Programming Summer Internship provides a master's-level student with the opportunity to apply programming, data science, automation, and emerging technologies to real-world challenges in clinical development. The intern will work alongside experienced statistical programmers and cross-functional partners on a defined project focused on improving the efficiency, quality, or scalability of clinical data and statistical programming processes.

Key Responsibilities

• Work with a project supervisor and subject-matter experts to understand a statistical programming, clinical data, automation, or technology-related business problem and translate it into clear project objectives and deliverables.
• Design, develop, test, and document a prototype, analytical workflow, automation solution, or reusable programming approach.
• Apply programming languages such as SAS, R, and/or Python to data processing, analysis, automation, validation, or clinical programming use cases.
• Explore opportunities to use automation, generative AI, metadata-driven approaches, or open-source technologies to improve programming efficiency and quality.
• Work with structured data and metadata and, where applicable, gain exposure to CDISC standards such as SDTM, ADaM, and Define-XML.
• Perform structured testing and validation to evaluate the accuracy, consistency, reliability, and limitations of developed solutions.
• Compare results against existing processes or established approaches and clearly document differences, assumptions, limitations, and areas requiring human review.
• Collaborate with statistical programmers and other cross-functional stakeholders to gather requirements, obtain feedback, and refine solutions.
• Prepare clear technical documentation and summarize project findings, lessons learned, and recommendations.
• Present the completed project and demonstrate key outcomes to Statistical Programming colleagues and other stakeholders at the conclusion of the internship.

Required Qualifications

• Currently enrolled in a Master's degree program in Statistics, Biostatistics, Data Science, Computer Science, Software Engineering, Mathematics, or a closely related quantitative discipline.
• Must be continuing in the Master's degree program following completion of the internship.
• Experience programming in at least one of the following: SAS, R, or Python.
• Foundational knowledge of data manipulation, analytical programming, statistical computing, software development, or related quantitative methods.
• Demonstrated ability to learn new programming languages, tools, and technical concepts.
• Strong analytical and problem-solving skills with attention to detail.
• Strong written and verbal communication skills.

Preferred Qualifications and Experience

• Experience with two or more of SAS, R, and Python.
• Familiarity with Git/GitHub, Visual Studio Code, automated testing, or reproducible programming workflows.
• Knowledge of clinical trial data or experience working with healthcare, life sciences, or other regulated data.
• Familiarity with CDISC standards, particularly SDTM, ADaM, Define-XML, or clinical trial metadata.
• Experience or coursework involving generative AI, large language models, automation, software testing, APIs, XML/JSON, or data visualization.
• Interest in applying emerging technology to improve clinical development and statistical programming processes.

Core Competencies

• Analytical Thinking: Able to break down complex problems, evaluate evidence, and develop practical solutions.
• Programming and Technical Learning: Demonstrates a strong foundation in programming and the ability to quickly learn new languages, tools, standards, and technologies.
• Innovation and Curiosity: Curious about emerging technologies and willing to explore new approaches to improve existing processes.
• Quality and Attention to Detail: Produces accurate, well-documented work and recognizes the importance of testing, validation, traceability, and reproducibility.
• Problem Solving: Able to identify issues, investigate root causes, compare alternative approaches, and recommend appropriate solutions.
• Collaboration: Works effectively with mentors, statistical programmers, and cross-functional partners and incorporates feedback constructively.
• Communication: Able to explain technical concepts, methods, findings, and limitations clearly to both technical and non-technical audiences.
• Accountability and Initiative: Self-motivated and able to manage assigned work, communicate progress, and deliver agreed project objectives within the internship timeline.
• Responsible Use of Technology: Understands the importance of data privacy, appropriate human oversight, and responsible use of automation and AI in a regulated environment.