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

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. Project Overview: The co-op will develop and benchmark structure-aware models that combine compound structures, physicochemical properties, and initial in vivo PK/TK data to predict exposure at untested dose levels based on public and internal data. The project will evaluate whether AI, ML, and mechanistic hybrid approaches improve prediction accuracy, uncertainty estimation, and detection of nonlinear exposure relative to standard empirical and mechanistic baselines. 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 Preferred Skills: Current Employees apply HERE Current Contingent Workers apply HERE US and Puerto Rico Residents Only: Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities. Please click here if you need an accommodation during the application or hiring process. 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. 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 Search Firm Representatives Please Read Carefully Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails. 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 *A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.