Senior Scientist Computational Chemistry

Merck•Upper Gwynedd Township, PA

6d•$129,000 - $203,100•Onsite

About The Position

Merck is seeking a creative, self-motivated computational chemist and data scientist to join the Modeling and Informatics group at its West Point, Pennsylvania site. The group is part of Discovery Chemistry and uses predictive sciences, AI/ML, and structure-based drug design to support therapeutic discovery. The role partners with multidisciplinary discovery teams locally and globally to invent novel medicines, applying advanced computational techniques to drug discovery programs and developing computational methods, algorithms, and predictive tools. This hybrid-in-scope role balances direct early-discovery project support (50%) with data science and machine-learning model design and deployment (50%). The successful candidate will support molecule design and optimization, build predictive models and automated workflows, and help advance computational chemistry methods. The position is on-site at the West Point facility and is not remote.

Requirements

PhD in Computational Chemistry, Chemoinformatics, Chemistry, Data Science, or equivalent, with a computational or data science emphasis.
Proficiency in molecular modeling techniques, including docking, molecular simulation, homology modeling, quantum mechanics, and ligand- and structure-based design.
Proficiency in a Linux environment.
Proficiency in scientific Python programming, such as RDKit, OpenEye toolkits, Pandas, NumPy, and SciPy.
Hands-on experience in applied machine learning, statistical modeling, or deep learning using tools such as scikit-learn, PyTorch, or TensorFlow, applied to chemical, biological, or physical datasets.
Ability to communicate, collaborate, and deliver results as a member of multidisciplinary teams.
Desire to learn and innovate.
Ability to manage multiple priorities across project delivery and method development.
Strong oral and written communication skills, including the ability to explain complex machine-learning and computational concepts to non-specialists.
Evidence of creatively applying computational approaches to pharmaceutical problems.
Track record of recent publications and/or presentations and an external presence.

Nice To Haves

Demonstrated impact on molecule design in a biopharmaceutical or biotechnology industry environment.
Expertise with commercial modeling software packages such as Schrödinger, MOE, or OpenEye.
Experience with modern software development practices, including Git/GitHub, Docker containerization, and cloud environments such as AWS or Google Cloud.
Experience developing and deploying interactive data visualization applications or web tools, such as Spotfire.
Familiarity with generative AI for de novo molecule generation, geometric deep learning, or large language models for scientific data.
Experience with automated workflow orchestration tools such as KNIME or Pipeline Pilot.

Responsibilities

Serve as the primary computational chemistry partner on multidisciplinary discovery teams, supporting candidate design and optimization.
Design molecules using computational drug-discovery techniques, including structure- and ligand-based design, molecular docking, conformational sampling, molecular dynamics, and quantum mechanics.
Perform informative data analysis, multi-parameter optimization, and molecular visualization.
Design protein constructs and assess binding-site ligandability.
Design ligand libraries, conduct high-throughput virtual screening, and perform targeted library enumeration.
Translate computational hypotheses and model predictions into actionable synthetic targets for medicinal chemistry teams.
Communicate computational insights to chemistry and adjacent teams to inform project decisions.
Partner with the Data Science team to design, build, benchmark, and deploy predictive AI/ML models for molecular properties, ADMET endpoints, and potency.
Develop and deploy automated computational workflows and reusable Python toolkits or pipelines for cheminformatics and structural modeling.
Explore and implement machine-learning methods, including generative chemistry models, active learning, and foundation models for molecular design.
Collaborate with Cheminformatics, Data Science, and IT teams to scale computational chemistry tools and integrate models into enterprise software platforms.
Establish best practices for scientific code versioning, reproducible data analysis, and model validation.
Share knowledge and reusable workflows, support colleagues, and contribute to departmental wellbeing.
Mentor colleagues in emerging data science techniques and modern computational workflows.