Senior Computational Chemist

Key Responsibilities

• Chemistry Data Enablement (Data Science / Data Engineering): Lead the design, curation, and application of high-quality chemistry and pharmacology datasets used across multiple discovery programs. Architect and deploy data pipelines and transformations that enable scalable, reproducible data analytics and modeling. Set expectations for data quality and fitness-for-purpose in collaboration with scientific and platform stakeholders.
• Applied ML and Cheminformatics: Independently frame ambiguous medicinal chemistry problems into clear computational strategies and decision-support analyses. Drive the application of ML and Cheminformatics models to high-impact drug discovery questions. Critically assess model performance and limitations, guiding appropriate interpretation and use in project decisions.
• Rapid Prototyping & Front-End Development: Deliver robust, reusable, and well-documented scientific software aligned with modern best practices. Lead rapid prototyping of chemistry and cheminformatics applications to validate workflows and user experience with end users. Partner closely with chemists and software teams to evolve successful prototypes into production-ready solutions.
• Cross-Functional Collaboration & Delivery: Effectively bridge chemistry, data science, and software perspectives to align stakeholders around robust solutions. Influence projects through technical insight, credibility, and data-driven recommendations. Contribute to the scientific culture of MLCS through knowledge sharing, internal talks, and cross-team collaboration.

Required Qualifications

• Ph.D. in Computational Chemistry, Cheminformatics, Computer Science, or related fields; or M.S. with substantial relevant industry experience.
• Deep understanding of cheminformatics concepts, including molecular representations, similarity methods, QSAR, virtual screening, chemical spaces.
• Experience analyzing large, complex chemistry-related datasets.
• Strong proficiency in Python and cheminformatics toolkits (RDKit or equivalent).
• Experience with standard data science packages (numpy, pandas, etc).
• Prior experience applying ML models in real-world discovery projects.
• Practical experience with rapid application or UI prototyping in a scientific context.

Preferred Qualifications

• Prior experience in pharmaceutical or biotech drug discovery environments.
• Familiarity with deploying and scaling scientific workflows and applications.
• Experience with database design and processing (SQL, RestAPI).
• Experience in large-scale data handling and developing data pipelines.
• Familiarity developing code in collaborative manner (GitHub, code review & sharing).
• Familiarity working in HPC (e.g. SLURM) or cloud-based (AWS, GCP) environments.
• Familiarity with efficient use of agentic coding environments (GitHub Copilot or equivalent).