2027 Future Talent Program – Translational Sciences and Outsourcing – Co-op

Merck; Boston (MA); Hybrid; USD 39,000 - 111,000; Full time

Job summary

Merck's Future Talent Program invites a motivated student to join the Translational Sciences and Outsourcing (TSO) team in PDMB for a 6-month co-op. Work on AI-enabled exposure prediction across dose levels in preclinical species, combining data curation, ML, and scientific communication to inform DMPK and NDS decisions.

You will collaborate with Data Science, PKPD, DMPK and toxicology teams while applying modern AI/ML methods to real PK/TK data with relevance to preclinical study design.

Qualifications

• Enrolled in a Master’s or Ph.D. program in a quantitative field.
• Experience with ML, statistical modeling, and scientific computing.

Responsibilities

• Curate cross-dose PK/TK datasets with provenance and holdout splits.
• Perform TK analysis using exposure endpoints (AUC, Cmax).
• Benchmark AI-enabled and hybrid models against baselines.
• Assess uncertainty and nonlinear PK behavior.
• Create clear plots and summaries for stakeholders.
• Share outcomes via presentations and publications.

Skills

Python

PyTorch

scikit-learn

NumPy

pandas

ML

data analysis

Education

Master's or PhD in data science or related quantitative field

Tools

KNIME

CS QE/QSAR tooling

ChemProp

Job description

Job Description

The Future Talent Program features Cooperative (Co-op) education that lasts up to 6 months 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 student to join the Translational Sciences and Outsourcing (TSO) team within the Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) department. The successful candidate will contribute to a high-impact translational modeling project focused on AI-enabled exposure prediction across dose levels in preclinical species. The role will combine data curation, machine learning, model benchmarking, and scientific communication to predict toxicokinetics (TK) that supports DMPK (drug metabolism and pharmacokinetics) and NDS (nonclinical drug safety) decision‑making when only limited initial in vivo data are available.

This co‑op provides an opportunity to work alongside Data Science, PKPD, DMPK, preclinical toxicology and related scientists while applying modern AI/ML methods to real pharmaceutical PK/TK data with direct relevance to preclinical study design and therapeutic window assessment.

Primary 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.

Required Education

• 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.

Required Skills

• 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.

Preferred Skills

• 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.

Learning Opportunities

The co‑op will gain hands‑on experience applying modern AI/ML methods to real pharmaceutical PK/TK data with direct impact on preclinical decision‑making. The role will 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.

Expected Deliverables

• A curated cross‑dose preclinical PK/TK dataset with provenance, quality checks, and holdout splits suitable for reuse by follow‑on efforts.
• A documented benchmark comparing AI‑enabled and hybrid models against empirical and mechanistic baselines.
• A model‑comparison report identifying where AI/hybrid approaches add value over existing approaches and where they do not. A final presentation summarizing project objectives, methods, results, limitations, and recommendations for next steps, with external publications.

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

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

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

Employee Status

Intern/Co‑op (Fixed Term)

Relocation

No relocation

VISA Sponsorship

No

Travel Requirements

No Travel Required

Flexible Work Arrangements

Hybrid

Shift

1st - Day

Valid Driving License

No

Hazardous Material(s)

N/A

Job Posting End Date

10/9/2026

Requisition ID

R412935

EEO and Diversity

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

We are proud to be a company that embraces the value of bringing together, talented, and committed people with diverse experiences, perspectives, skills and backgrounds. The fastest way to breakthrough innovation is when people with diverse ideas, broad experiences, backgrounds, and skills come together in an inclusive environment. We encourage our colleagues to respectfully challenge one another’s thinking and approach problems collectively.

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