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Boston, MA

Internship

Hybrid

$39K/yr - $111K/yr

Intern

Merck is seeking a co-op student to join its Translational Sciences and Outsourcing team in Research and Development. The role develops and benchmarks AI-enabled and hybrid models to predict preclinical toxicokinetic exposure, supporting drug metabolism, pharmacokinetics, and nonclinical drug safety decisions.

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Responsibilities

Curate and quality-check preclinical cross-dose PK/TK datasets with clear provenance, reusable data definitions, and appropriate holdout splits to construct reusable benchmark dataset
Conduct TK analysis with exposure endpoints such as AUC and Cmax for different compound, dose, and route of administration to identify trends, nonlinearities, and data limitations
Develop and benchmark AI-enabled and hybrid modeling approaches against relevant baselines such as linear scaling, power/Emax models, QSAR/ChemProp-based methods, and mechanistic approaches where applicable
Evaluate model performance, uncertainty estimation, and ability to detect nonlinear PK behavior such as saturation or sex differences
Create clear visualizations, written summaries, and technical presentations for cross-functional stakeholders
Share project outcomes through internal and/or external presentation and publication

Qualification

Required

Currently pursuing a Master's or Ph.D. degree in data science, computer science, applied mathematics, statistics, computational chemistry, chemical or biomedical engineering, pharmaceutical sciences, or a related quantitative discipline
Research experience in machine learning, statistical modeling, scientific computing, or computational modeling
Strong Python programming skills, including scientific computing with NumPy, pandas, and scikit-learn
Experience with deep learning frameworks such as PyTorch or JAX
Solid foundation in machine learning, statistics, model evaluation, and reproducible analysis workflows
Ability to work with messy tabular or time-series scientific data and translate analytical findings into clear plots and written summaries
Effective written and verbal communication skills and ability to collaborate in a cross-functional research environment
Applied Mathematics, Chemical Informatics, Clinical Research, Computational Models, Computational Sciences, Data Analysis, Database Management, Data Science, Data Security, KNIME, Machine Learning (ML), Model Development, Neural Networks, Parameter Estimation, Project Management, Python (Programming Language), PyTorch, Quantitative Structure Activity Relationship (QSAR), Scientific Research, Scientific Software Development, scikit-learn, SciPy, Software Proficiency, Statistical Models

Preferred

Exposure to graph neural networks, molecular representation learning, ChemProp, QSAR modeling, or related cheminformatics methods
Familiarity with ODEs, dynamical systems, differentiable simulation, torchdiffeq, Neural ODEs, or mechanistic modeling concepts
Background or strong interest in pharmacokinetics, toxicokinetics, pharmacometrics, PBPK, ADME, or preclinical safety assessment
Interest in agentic AI or LLM-enabled tooling for scientific workflow automation
Fluent in leveraging state-of-the-art AI tools for boosting productivity, such as Claude Code, ChatGPT, Gemini, DeepSeek, etc

Benefits

Hybrid work arrangement
Hands-on experience applying modern AI/ML methods to real pharmaceutical PK/TK data with direct impact on preclinical decision-making
Build technical depth in molecular representation learning, model benchmarking, uncertainty estimation, PK/TK data analysis, and scientific communication in a cross-functional pharmaceutical R&D setting

Merck is a biopharmaceutical company that offers medicines and vaccines for various diseases.

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