Senior Scientist Computational Chemistry

Job Description

Position Description

Our company is seeking a creative, self-motivated computational chemist and data scientist with exceptional interpersonal and problem-solving skills to join the Modeling and Informatics group at our West Point, PA site. We are expanding our capability in computational medicinal chemistry, predictive data science, and machine learning to support our future organizational strategy for therapeutic discovery.

Modeling and Informatics is a sub-team of Discovery Chemistry, who employ state-of-the-art capabilities in predictive sciences, AI/ML, and structure-based drug design to accelerate our invention of therapeutics for patients. We are an integral arm of both Discovery and Process Chemistry, and we collaborate seamlessly with teams across the globe to invent better molecules faster. With an unbiased approach to drug modalities, in-house access to the newest screening techniques, and world-class medicinal chemistry, we have a wealth of opportunities to impact our pipeline and ultimately save and improve patient lives.

The successful candidate will join multidisciplinary, highly collaborative discovery teams both locally and across the global network to invent novel medicines. This role is uniquely designed as a hybrid position, balancing direct early discovery project support (~50%), design, and deployment of data science and machine learning models (~50%). They will apply advanced computational techniques to active drug discovery programs and develop innovative computational methods, algorithms, and predictive tools to solve foundational problems in drug design.

Key Responsibilities:

Discovery Project Support:

• Serve as the primary computational chemistry partner on multidisciplinary discovery teams to drive candidate design and optimization through:
• Design of molecules utilizing existing computational drug discovery tools and techniques (structure- and ligand-based design, molecular docking, conformational sampling, molecular dynamics, and quantum mechanics).
• Informative data analysis, multi-parameter optimization (MPO), and molecular visualization.
• Protein construct design and binding-site ligandability assessment.
• Ligand library design, virtual screening (HTVS), and targeted library enumeration.
• Translating complex computational hypotheses and non-obvious model predictions into actionable synthetic targets for medicinal chemistry teams.
• Influencing teams in chemistry and adjacent functionalities by effectively communicating non-obvious insights from computational approaches to drive project decisions and inflections.

Data Science & Machine Learning Modeling/Development:

• Partner with existing Data Science team to design, build, benchmark, and deploy predictive AI/ML models (e.g., QSAR, deep learning, graph neural networks) for molecular property prediction, ADMET endpoints, and potency predictions.
• Develop and deploy automated computational workflows and reusable Python toolkits/pipelines for cheminformatics and structural modeling.
• Explore and implement cutting-edge machine learning methodologies, including generative chemistry models, active learning, and foundation models for molecular design.
• Collaborate closely 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.

Departmental Leadership & Culture:

• Contributing to the overall wellbeing of our department by sharing knowledge, reusable workflows, and being invested in the success of your peers.
• Mentoring colleagues in emerging data science techniques and modern computational workflows.

Position Qualifications:

Education Minimum Requirement:

• PhD in Computational Chemistry, Chemoinformatic, Chemistry, Data Science, or equivalent, with a computational/data science emphasis.

Required Experience and Skills:

• Proficiency in general molecular modeling techniques (docking, molecular simulation, homology modeling, quantum mechanics, ligand- and structure-based design) and Linux environment.
• Proficiency in scientific Python programming (e.g., RDKit, OpenEye toolkits, Pandas, NumPy, SciPy).
• Demonstrated hands-on experience in applied machine learning, statistical modeling, or deep learning (e.g., scikit-learn, PyTorch, TensorFlow) applied to chemical, biological, or physical datasets.
• Ability to communicate, collaborate, and deliver results as a member of multidisciplinary teams.
• A strong desire to learn and to innovate.
• Ability to manage multiple priorities simultaneously across project delivery and method development.
• Strong oral and written communication skills with the ability to convey complex ML/computational concepts to non-specialists.
• Evidence of creative application of computational approaches to problems of pharmaceutical interest.
• Track record of recent publications and/or presentations and external presence.

Preferred Experience and Skills:

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