Job Description

The Future Talent Program features internships that last up to 12 weeks and will include one or more projects. These opportunities in our Research and Development Division can provide you with great development and a chance to see if we are the right company for your long-term goals.

We are seeking a highly motivated intern student to join the Translational Sciences and Outsourcing (TSO) Data Science team within our Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) department. The intern will contribute to the development and rigorous benchmarking of AI/ML methods for pharmacokinetic curve prediction, partnering with scientists to generate insights that support decision-making across drug discovery and development.

The ideal candidate will combine strong machine learning implementation skills with scientific rigor, reproducible research practices, scientific curiosity, and a collaborative mindset, along with an interest in AI, pharmacokinetics, cheminformatics, and computational drug discovery. This internship provides an opportunity to apply modern AI/ML to large-scale pharmaceutical data, learn how computational methods are evaluated in an industrial research setting, and contribute to a reproducible, publication-oriented study with potential impact across the PDMB research portfolio.

Project Overview:
The intern will benchmark existing AI/ML methods for predicting pharmacokinetic concentration-time profiles using a large-scale internal dataset. The project will determine how model choice, data-splitting strategy, chemical similarity, compound novelty, and variation across data sources affect predictive performance and prospective generalization. The work is scoped to produce a rigorous, reproducible benchmark study and, subject to scientific results and internal review, a paper suitable for submission to a peer-reviewed journal or scientific conference.

Primary 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, and quantify performance across compound novelty levels to identify data leakage and limitations in prospective generalization.
Collaborate with scientists to interpret results and ensure that 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.

Required Skills:

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 (e.g., ICML, ICLR, NeurIPS).
Strong Python programming skills, with 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, build reproducible analysis pipelines, and understand how data leakage and evaluation design affect model generalization.
Demonstrated interest in preparing a publishable computational drug discovery benchmark, with experience in designing comparative studies, building reproducible research workflows, and clearly interpreting and communicating technical results.
Strong communication skills and ability to summarize technical results clearly.

Preferred Skills:

Experience with pharmacokinetics, pharmacometrics, ADMET, or drug discovery.
Familiarity with PK 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 is beneficial.
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.

Learning Opportunities:

The intern will gain exposure to:

Pharmacokinetics and its applications in drug discovery.
Machine learning methods for concentration-time curve prediction.
Molecular representations, similarity analysis, chemical clustering, and rigorous benchmark design.
Reproducible research and cross-functional collaboration within a pharmaceutical research organization.
Scientific writing, communication, and presentation skills.

Expected Deliverables:

A reproducible evaluation workflow with documented datasets, train/test splits, model configurations, metrics, and visualizations.
A final presentation summarizing project objectives, methods, results, scientific conclusions, limitations, and recommended next steps.
A draft manuscript describing the scientific question, benchmark design, comparative results, chemical-novelty analyses, limitations, and conclusions, prepared with the project team and subject to internal publication review.

Please note that this position may be closed before the posted end date or may remain open longer, at the discretion of the company.

Salary range:
The salary range for this role is $39,108 through $111,111.

FTP2027

RL2027

Required Skills:

Preferred Skills:

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Employee Status:

Intern/Co-op (Fixed Term)

Relocation:

VISA Sponsorship:

Travel Requirements:

Flexible Work Arrangements:

Not Applicable

Shift:

Valid Driving License:

Hazardous Material(s):

Job Posting End Date:

10/9/2026

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