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United States

Internship

Remote

$32/hr - $55/hr

Intern

Bristol Myers Squibb offers a master’s-level internship applying programming, data science, automation, and emerging technologies to clinical development challenges. The intern will develop, test, document, and present a project that improves the efficiency, quality, or scalability of clinical data and statistical programming processes.

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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

Qualification

Required

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

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
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