Discovery Bioanalytics Intern: Predictive ML for Drug Discovery

MSD Malaysia; West Point (PA); On-site; USD 39,000 - 111,000; Full time

Job summary

MSD Malaysia is offering a 12-week Future Talent Program internship in our Discovery Bioanalytics group, a part of Drug Metabolism and Pharmacokinetics. You will contribute to data analytics, machine learning, and predictive modeling within a real pharmaceutical workflow.

The role supports improving efficiency and data-driven decision-making in discovery workflows, with hands-on experience using historical data and building predictive tools under guidance from discovery scientists.

Qualifications

• Currently pursuing a Bachelor's degree in Data Science, Computer Science, Chemical/Biological/Biomedical or Computational Engineering, Chemistry, Biology, Pharmacology or related discipline.
• Strong foundation in data analysis and problem solving.
• Interest in applied AI/ML, predictive modeling, or data-driven decision making.

Responsibilities

• Source historical compound and analytical method data related to chemical properties and liquid chromatography.
• Develop a predictive machine learning model to serve as a recommendation framework.
• Collaborate with bioanalytical scientists to evaluate and validate the predictive accuracy of the model.
• Explore opportunities to expand the scope of the model to address additional method development parameters.

Skills

Data science

Applied AI/ML

Predictive modeling

Python

Excel

R

Communication

Independent working

Education

Bachelor's or equivalent in Data Science or related field

Tools

Python

R

Excel

Job description

MSD Malaysia is offering a 12-week Future Talent Program internship in our Discovery Bioanalytics group, a part of Drug Metabolism and Pharmacokinetics. You will contribute to data analytics, machine learning, and predictive modeling within a real pharmaceutical workflow.

The role supports improving efficiency and data-driven decision-making in discovery workflows, with hands-on experience using historical data and building predictive tools under guidance from discovery scientists.