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About the Role

This internship within the Translational Sciences and Outsourcing (TSO) Data Science team focuses on developing and benchmarking AI/ML methods for pharmacokinetic curve prediction. The role offers an opportunity to apply modern AI/ML to large-scale pharmaceutical data, learn industrial research evaluation methods, and contribute to a publication-oriented study with potential impact across the research portfolio.

Responsibilities

Analyze and prepare large-scale pharmacokinetic and molecular datasets for reproducible modeling and evaluation.
Implement and benchmark classical machine learning baselines, molecular-structure-based models, and time-series or mechanism-informed approaches for pharmacokinetic curve prediction.
Design and compare various evaluation settings (random, scaffold, time-based, chemical-similarity-aware) to quantify performance across compound novelty levels.
Collaborate with scientists to interpret results and ensure scientific meaningfulness of benchmark conclusions.
Document methods and results, contributing to a draft manuscript or technical report.
Present project outcomes at the end-of-summer intern symposium.

Requirements

PhD student in computational chemistry, cheminformatics, machine learning, computer science, computational biology, statistics, or a related field.
Strong record of publication in top-tier, peer-reviewed scientific journals or machine learning conferences.
Strong Python programming skills with experience using machine learning or deep learning frameworks (e.g., PyTorch).
Experience with model development and evaluation.
Ability to work with complex scientific datasets, build reproducible analysis pipelines, and understand data leakage and evaluation design effects on model generalization.
Demonstrated interest in preparing a publishable computational drug discovery benchmark.
Experience in designing comparative studies, building reproducible research workflows, and interpreting/communicating technical results.
Strong communication skills with the ability to summarize technical results clearly.

Skills

Python
Machine Learning
Deep Learning
PyTorch
Reproducible Research
Data Analysis
Scientific Writing
Communication
Pharmacokinetics
Pharmacometrics
ADMET
Drug Discovery
Cheminformatics
Molecular Representations
Graph Neural Networks
Chemprop
Uni-Mol
RDKit
QSAR
ADME Modeling
Molecular Similarity Analysis
Tanimoto Similarity
Scaffold Splitting
Chemical Clustering
Out-of-Distribution Evaluation
Time-Series Modeling
ODE Models
Neural ODEs
Physics-Informed Machine Learning

Location

Rahway, NJ, USA

Work Type

Intern/Co-op
Fixed Term

Experience Level

PhD student

Education Level

Salary/Compensations

$39,108 - $111,111

Benefits

Development opportunities
Chance to see if the company is the right fit for long-term goals
Opportunity to apply modern AI/ML to large-scale pharmaceutical data
Learn how computational methods are evaluated in an industrial research setting
Contribute to a reproducible, publication-oriented study
Exposure to pharmacokinetics and its applications in drug discovery
Exposure to machine learning methods for concentration-time curve prediction
Exposure to molecular representations, similarity analysis, chemical clustering, and rigorous benchmark design
Exposure to reproducible research and cross-functional collaboration within a pharmaceutical research organization
Exposure to scientific writing, communication, and presentation skills

About the Company

Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA.
Committed to inclusion, ensuring candidates can engage in a hiring process that exhibits their true capabilities.
Embraces the value of bringing together talented and committed people with diverse experiences, perspectives, skills and backgrounds.
Believes the fastest way to breakthrough innovation is when people with diverse ideas, broad experiences, backgrounds, and skills come together in an inclusive environment.
Encourages colleagues to respectfully challenge one another’s thinking and approach problems collectively.

Equal Opportunity

As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics.
As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities.
For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit: EEOC Know Your Rights, EEOC GINA Supplement.
Learn more about your rights, including under California, Colorado and other US State Acts.
San Francisco Residents Only: We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance.
Los Angeles Residents Only: We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance.