Jobs at Alexion Pharmaceuticals

This co-op will focus on developing a closed-loop computational and experimental platform that integrates state-of-the-art computational tools and experimental testing to accelerate chiral ligand development and optimization. SPD is a multidisciplinary team of scientists supporting synthetic drug substance, drug product, and analytical development across Alexion’s synthetic portfolio, spanning preclinical development through commercial launch.This position offers a unique opportunity to gain hands-on experience in applying computational chemistry and AI technologies to real-world challenges in diastereoselective catalyst design, ligand discovery, and computationally guided experimental optimization

• Partner with synthetic chemists to identify key selectivity challenges.
• Co-design chemically reasonable training sets for computational modeling.
• Validate computational predictions against experimental results.
• Perform DFT transition-state calculations to determine energies and geometries of chiral ligands.
• Develop machine learning surrogate models to predict diastereomeric ratios.
• Use molecular descriptors to support predictive modeling and ligand optimization.
• Implement Bayesian optimization campaigns to identify optimal ligand candidates.
• Balance ligand selectivity, reactivity, and synthetic feasibility during optimization.
• Test computational predictions through experimental studies.
• Use experimental results to refine subsequent computational design cycles.
• Build and maintain the closed-loop DFT → ML → Bayesian Optimization → Experimental pipeline.
• Document computational workflows to ensure reproducibility, scalability, and knowledge transfer.
• Communicate scientific challenges, methodologies, and results through regular team updates.
• Prepare and deliver a final presentation summarizing computational and experimental findings.

• Currently enrolled in a Ph.D. program in Synthetic Organic Chemistry, Computational Chemistry, or a related field.
• Focus or research experience in Computational Chemistry is preferred.
• Practical experience with DFT / quantum chemistry calculations.
• Understanding of transition-state theory and energy calculation workflows.
• Hands-on laboratory experience in synthetic organic chemistry, catalysis, or related disciplines.
• Ability to safely perform experiments and interpret experimental results.
• Understanding of organic stereochemistry and asymmetric catalysis.
• Knowledge of chiral ligand design concepts.
• Programming experience with Python or another programming language.
• Familiarity with relevant scientific computing libraries.
• Foundational knowledge of machine learning, including training, validation, overfitting, regression, and classification.
• Strong written and verbal communication skills.
• Ability to work effectively in a collaborative research team.
• Strong work ethic, self-motivation, and willingness to learn.
• Must be available for the full 6-month co-op term during standard business hours.
• U.S. work authorization is required at the time of application.
• OPT sponsorship is not provided; CPT-authorized candidates may be considered subject to eligibility requirements.
• Prior cheminformatics or data-driven research experience is preferred.
• Experience with Bayesian optimization or active learning pipelines is preferred.
• Familiarity with molecular descriptors, molecular fingerprints, and SAR/selectivity modeling is preferred.
• Experience with model interpretability or feature-attribution methods applied to chemical or materials data is a plus.