AI/ML Automation Pipelines Intern

Daiichi Sankyo
Location
Unknown
Job Type
Full-time
Posted
November 8, 2026

Job Description

We are currently seeking an AI/ML Automation Pipelines Intern for Summer 2027. This position works for approximately 37.5 hours per week.

Responsibilities

The AI/ML Automation Intern will explore how AI and machine learning can streamline programming workflows in clinical trial reporting. The focus will be on building proof-of-concept automation pipelines that use Python and ML models to improve efficiency, accuracy, and standardization.

Objectives

  • Learn how AI/ML can be integrated into clinical data workflows.
  • Build automation pipelines to reduce manual coding and enhance prediction accuracy.
  • Apply Python programming and ML frameworks (e.g., scikit-learn, LangChain) to real-world data challenges.

Key Deliverables

  • Develop a prototype ML-based code recommender for TLF (Tables, Listings, Figures) generation.
  • Automate study metadata extraction from protocols or SAPs using NLP models.
  • Create a Python-based monitoring dashboard for QC metrics across multiple studies.
  • Present findings and demonstrate automation pipeline in a showcase session.

Qualifications

Currently pursuing Masters or PhD in Biostatistics, Computer Science or relevant fields with analytics focus. Strong experience with R, R Shiny, Python and AI/ML is required.

Frequently Asked Questions

What is this internship and when does it take place?
This is an AI/ML Automation Pipelines Intern position at Daiichi Sankyo for Summer 2027, working approximately 37.5 hours per week.
What qualifications do I need to apply?
You must be currently pursuing a Master's or PhD in Biostatistics, Computer Science, or a relevant field with an analytics focus. Strong experience with R, R Shiny, Python, and AI/ML is required.
What will I be working on during the internship?
You will explore how AI and machine learning can streamline programming workflows in clinical trial reporting. You will build proof-of-concept automation pipelines using Python and ML models to improve efficiency, accuracy, and standardization.
What are the key deliverables expected from the intern?
1. Develop a prototype ML-based code recommender for Tables, Listings, and Figures (TLF) generation. 2. Automate study metadata extraction from protocols or SAPs using NLP models. 3. Create a Python-based monitoring dashboard for QC metrics across multiple studies. 4. Present findings and demonstrate the automation pipeline in a showcase session.
What tools or frameworks will I use?
You will apply Python programming and ML frameworks, such as scikit-learn and LangChain, to real-world clinical data challenges.

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Job Information

Source: manual
AI Relevance: 60/100 (Relevant)
Remote Type: onsite
Skills & Tags:
daiichi sankyo machine learning NLP biostatistics clinical

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