Data Science Intern: Drug Discovery & ML Pipelines

Merck; West Point (PA); On-site; USD 40,000 - 111,000; Full time

Job summary

Merck, through the Future Talent Program, offers a 10–12 week data science internship in the Quantitative Biosciences department. You will work alongside seasoned pharmaceutical scientists, applying statistical analyses and building data pipelines to support discovery and translational strategies in multiple therapeutic areas.

The role emphasizes collaboration, rigorous data exploration, and communication of progress in internal meetings, with opportunities to present final results to scientists

Qualifications

• Currently enrolled in a full-time degree program in Data Science / Computer Science or related Bioscience with substantial computational coursework.
• Completed at least 2 years toward BS/BA or at least 1 year toward MS/PhD by internship start.
• Available to work full-time for 10–12 weeks beginning May or June 2027.
• Superior verbal and written communication skills and a strong academic record.

Responsibilities

• Perform statistical analyses of data from basic drug research.
• Help develop data pipelines to move and analyze diverse biological data.
• Develop computational methods and ML models to improve drug discovery.
• Wrangle and analyze structured and unstructured data for translational strategies.
• Engineer data pipelines between reporting environments and core databases.
• Communicate data progress in internal meetings and present results.
• Collaborate with scientists in the Quantitative Biosciences department.

Skills

SQL Proficiency

Cloud Data Warehouses

Python

R

MATLAB

Data Visualization

Education

Full-time degree in Data Science/Computer Science or related Bioscience

Tools

Amazon Redshift

AWS S3

DBeaver

SQL Workbench

Visual Analytics

Job description

Merck, through the Future Talent Program, offers a 10–12 week data science internship in the Quantitative Biosciences department. You will work alongside seasoned pharmaceutical scientists, applying statistical analyses and building data pipelines to support discovery and translational strategies in multiple therapeutic areas.

The role emphasizes collaboration, rigorous data exploration, and communication of progress in internal meetings, with opportunities to present final results to scientists