AI & Data Genetics Co-op: Patient Insights

Merck; Cambridge (MA); On-site; USD 40,000 - 111,000; Full time

Job summary

Merck’s Future Talent Program in the Data, AI, and Genome Sciences department offers a six-month co-op in the Patient Data Learning group in Cambridge, MA. You will work on computational projects to develop skills and evaluate long-term fit with the company.

The role seeks highly motivated students with computational skills, eager to implement tools for feasibility questions and contribute to precision genetics research.

Qualifications

• Currently enrolled in a Master's program (or PhD encouraged) in a biomedical/clinical informatics related field.
• Strong software engineering fundamentals and experience with local LLMs and agent development practices.
• Proficiency in Python and SQL, with database-agnostic querying capability.
• Experience with querying structured EMR/EHR data or demonstrated interest in clinical applications.

Responsibilities

• Design and build agent-assisted systems to explore phenotypes and clinical profiles in EHR-linked biobanks.
• Reuse and customize AI language models to translate clinical questions into machine queries.
• Create guardrails and evals to measure model and agent reliability.
• Assist applying these tools to feasibility questions in a collaborative setting.

Skills

Biomedical Applications

Biomedical Informatics

Biomedical Sciences

Clinical Information Systems

Clinical Research

Data Analysis

Database Management

Data Engineering

Data Science

Data Security

Data Visualization

Econometrics

Epidemiology

Epidemiologic Methods

Medical Informatics

NLP

Python

R

Reliability Analysis

Tidyverse

Education

Master's in Clinical Informatics / related field

Tools

Databricks

IBIS

Job description

Merck’s Future Talent Program in the Data, AI, and Genome Sciences department offers a six-month co-op in the Patient Data Learning group in Cambridge, MA. You will work on computational projects to develop skills and evaluate long-term fit with the company.

The role seeks highly motivated students with computational skills, eager to implement tools for feasibility questions and contribute to precision genetics research.