2027 Future Talent Program - Nonclinical Drug Safety Data Scientist - Intern

Quick Facts

• Program: Future Talent Program internship (Intern/Co-op, fixed term)
• Duration: Up to 12 weeks (must be available for 10–12 weeks)
• Start: Beginning May 2027
• Location: West Point, PA or Boston, MA
• Travel: No travel required
• Relocation: No relocation
• Visa sponsorship: No

Description

The Future Talent Program features internships lasting up to 12 weeks and includes one or more projects in the Research and Development Division. NDS (Non-Clinical Drug Safety) advances high-quality drug candidates by defining non-clinical safety and selectivity of lead compounds. Interns evaluate Lead Op candidates and preclinical toxicity, support mechanistic understanding of drug-induced toxicity, assess implications for human safety, and contribute to research and regulatory support for drug registration.

Responsibilities

• Evaluate lead compounds and preclinical toxicity of drug development candidates
• Support mechanistic understanding of drug-induced toxicity
• Assess implications for human safety
• Contribute to collaborative animal model development, veterinary medical and animal care, and research facility management
• Support regulatory questions related to drug registration

Requirements

• Currently enrolled in a minimum of a BS/BA in applied math, computer science, chemistry, physics, computer engineering, biomedical engineering, or related disciplines
• Available for 10–12 weeks beginning May 2027

Benefits

• Opportunity to collaborate with global teams and dedicated colleagues
• Project-based experience to develop and expand your career
• Salary range: $39,108 through $111,111

Preferred Qualifications

• GPA 3.0 or higher
• Strong analytical and communication skills
• Demonstrated ability to learn new technologies
• Proficiency in Python with pandas, numpy, scipy, scikit-learn
• Familiarity with Streamlit, Dash, Plotly for UI/dashboard creation
• Hands-on experience analyzing discrete, continuous, and time-series data
• Familiarity with structured and unstructured data sources
• Experience with statistics, discrete math, and probabilistic modeling
• Familiarity with supervised and unsupervised statistical learning methods