• Weekly Schedule (how many hours/days): 40 hours
• Shift: 8 hours
• Interview required? Yes
• Resume required? Yes
• Any specific skills, requirements or experience you want in a candidate: Computational Chemistry skills; PH.D Required - Chemistry, Toxicology, Cheminformatics
• Any additional Medical Surveillance testing required? No
• Will non-local/remote candidates be considered? No
• Will they be driving? No


Responsibilities:
• Develop and apply chemistry structure-based predictive models for assessing safety profiles of early-stage discovery molecules
• Assess potential mechanisms of toxicity by assessing ligand-protein binding affinity using protein structure alignment, binding pocket evaluation, and molecular docking
• Work collaboratively with internal and external cross-functional multidisciplinary teams, collaborators, and consultants to implement in silico models to meet business needs
• Serve as a subject matter expert on in silico modelling for the team and other partners in R&D sub-functions
• Present the development and application of cheminformatics/bioinformatics approaches and novel ML/DL models internally and externally including scientific conferences
• Keep abreast of the latest scientific development in the areas of machine learning, cheminformatics, bioinformatics, and related fields and identify technologies to be applied internally
• Analyze complex datasets using machine learning approaches and interpret results to improve data-driven decision-making processes

Qualifications:
• Ph.D. degree in Cheminformatics, Computational Chemistry, Computational Biology, Bioinformatics, Toxicology, or a related field
• 3+ years of experience in developing machine learning models and applying cheminformatics/bioinformatics and AI for structure-based drug design, toxicology assessment, mechanism prediction
• Strong technical background in machine learning, deep learning, and statistical modeling with prior experience in applying these techniques to process, analyze and draw insights from complex datasets involving chemical compound structures and toxicity endpoints
• Demonstrated programming proficiency in Python and experience in utilizing machine learning libraries
• Demonstrated in-depth knowledge in toxicology, chemistry, chemical structure, QSAR, protein structure
• Knowledge in omics analysis and systems biology is a plus
• Demonstrated teamwork and project leadership skills to manage multiple projects on different timelines for stakeholders across the business
• Excellent communication and presentation skills to different stakeholder audiences