2027 Future Talent Program – Translational Sciences and Outsourcing – Intern

MSD • South San Francisco, CA

7d • $39,108 - $111,111

About The Position

The Future Talent Program offers internships lasting up to 12 weeks, with one or more projects, within the Research and Development Division. The intern will join the Translational Sciences and Outsourcing (TSO) Data Science team in the Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) department. The role focuses on developing and rigorously benchmarking AI/ML methods for predicting pharmacokinetic concentration-time profiles, working with scientists to generate insights that support decisions across drug discovery and development.

The project will benchmark existing AI/ML methods using a large internal dataset and examine how model choice, data-splitting strategy, chemical similarity, compound novelty, and differences across data sources affect predictive performance and prospective generalization. It is intended to produce a rigorous, reproducible benchmark study and, subject to results and internal review, a paper suitable for submission to a peer-reviewed journal or scientific conference.

The intern will prepare reproducible evaluation workflows and document datasets, splits, model configurations, metrics, and visualizations; present project outcomes at an end-of-summer intern symposium; and help prepare a draft manuscript with the project team, subject to internal publication review. The internship also offers exposure to pharmacokinetics, computational drug discovery, reproducible research, cross-functional collaboration, and scientific communication.

Requirements

• Be a PhD student in computational chemistry, cheminformatics, machine learning, computer science, computational biology, statistics, or a related field.
• A strong record of publication in top-tier, peer-reviewed scientific journals or machine learning conferences, such as ICML, ICLR, or NeurIPS.
• Strong Python programming skills and experience using machine learning or deep learning frameworks such as PyTorch or similar tools.
• Experience with model development and evaluation.
• Ability to work with complex scientific datasets and build reproducible analysis pipelines.
• Understand how data leakage and evaluation design affect model generalization.
• Demonstrated interest in preparing a publishable computational drug discovery benchmark, with experience designing comparative studies, building reproducible research workflows, and clearly interpreting and communicating technical results.
• Strong communication skills and ability to summarize technical results clearly.

Nice To Haves

• Experience with pharmacokinetics, pharmacometrics, ADMET, or drug discovery.
• Familiarity with pharmacokinetic concepts such as concentration-time curves, AUC, Cmax, clearance, half-life, compartment models, and NCA.
• Experience with molecular representations and cheminformatics tools, including SMILES, molecular fingerprints, graph neural networks, Chemprop, Uni-Mol, or RDKit.
• Familiarity with molecular property prediction, QSAR, or ADME modeling.
• Experience with molecular similarity analysis, such as Tanimoto similarity, scaffold splitting, chemical series clustering, or out-of-distribution evaluation.
• Experience with time-series modeling, ODE models, neural ODEs, or physics-informed machine learning.

Responsibilities

• Analyze large-scale pharmacokinetic and molecular datasets and prepare them for reproducible modeling and evaluation.
• Implement and benchmark classical machine learning baselines, molecular-structure-based models, and representative time-series or mechanism-informed approaches for pharmacokinetic curve prediction.
• Design and compare random, scaffold, time-based, and chemical-similarity-aware evaluation settings; quantify performance across compound novelty levels to identify data leakage and limitations in prospective generalization.
• Collaborate with scientists to interpret results and ensure benchmark conclusions are scientifically meaningful.
• Document methods and results and contribute to a draft manuscript or technical report.
• Present project outcomes at the end-of-summer intern symposium.
• Prepare a reproducible evaluation workflow documenting datasets, train/test splits, model configurations, metrics, and visualizations.
• Prepare a final presentation summarizing objectives, methods, results, scientific conclusions, limitations, and recommended next steps.
• Contribute with the project team to a draft manuscript on the scientific question, benchmark design, comparative results, chemical-novelty analyses, limitations, and conclusions, subject to internal publication review.

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree