Intern, Biometrics

The Role

This is a full-time summer internship based in Cambridge, MA from June 7, 2027 - August 20, 2027 . Candidates must be available for the entire duration of the program.

The Statistical Innovation & Data Analytics (SiDA) team is advancing AI-enabled statistical and clinical data solutions to accelerate data-driven decision-making, improve operational efficiency, and strengthen regulatory readiness across clinical development programs.

This role provides hands-on experience at the intersection of statistics, clinical data science, and AI engineering, with opportunities to work on:

• Clinical and statistical data analytics, including EDC, SDTM, ADaM, and translational data
• AI/ML research and applied statistical modeling for clinical development use cases
• LLM and agentic AI development, including domain-specific agents, tool integration, retrieval, and workflow orchestration
• Local and domain-specific model post-training, evaluation, and optimization for statistical and clinical applications
• AI-assisted statistical programming and analytics, exploring opportunities to automate and augment existing biometrics workflows
• Scalable and governed AI solutions, transitioning promising prototypes toward production-oriented applications that meet clinical data, security, quality, and compliance requirements

The student will contribute to the design, development, evaluation, and implementation of AI-enabled capabilities that support statisticians, statistical programmers, data scientists, and clinical development teams.

The role is particularly suited for students interested in going beyond general-purpose AI experimentation to understand how AI can be tailored, evaluated, and operationalized for statistical and clinical research workflows, with the goal of reducing repetitive work, accelerating time to insight, and enabling more agile use of clinical data.

Here's What You'll Do

• Develop and evaluate AI/ML and agentic solutions for clinical data analysis, statistical programming, and workflow automation, including tool-calling, RAG, and skill-based agent architectures.
• Explore LLM post-training, adaptation, and evaluation approaches to improve model performance for statistical and clinical research use cases.
• Design agentic workflows with appropriate human oversight, traceability, and reproducibility for use in regulated clinical development environments.
• Build scalable and modular AI pipelines that integrate clinical, statistical, and translational datasets.
• Develop interactive data visualizations, dashboards, and analytical applications to accelerate clinical insights and decision-making.
• Collaborate with statisticians, statistical programmers, data scientists, and scientists to translate clinical and business needs into practical technical solutions.
• Explore opportunities to automate and augment the clinical data lifecycle, including data processing, analysis, QC, interpretation, and reporting.
• Contribute to documentation, evaluation, validation considerations, and reusable AI capabilities that can scale across studies and programs.

Here's What You'll Bring to the Table (Preferred Qualifications)

• Master, doctoral or advanced undergraduate Student in statistics, biostatistics, computational biology, health informatics, data science or computer science related quantitative disciplines.
• Excellent statistical knowledge and quantitative skills with the ability to apply the knowledge to solve scientific and clinical problems.
• Experience in coding in Python (required) and R (preferred); demonstrated ability in cloud computing platforms, containerization, backend development, and SQL.
• Experience or demonstrated interest in GenAI/LLMs, machine learning, agentic systems, model post-training, or AI application development.
• Familiarity with one or more of the following is a plus: cloud computing, containerization, APIs/backend development, RAG, model evaluation, or AI deployment.
• Experience developing data visualizations, analytical applications, dashboards, or reusable computational tools is preferred.
• Interest in building practical, reliable, and scalable AI solutions for clinical or biomedical research.
• Strong analytical thinking, engineering mindset, curiosity, and written and verbal communication skills.
• Ability to learn quickly, work across disciplines, and translate emerging AI technologies into practical solutions.

Our Working Model

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