GitHub, git, subversion, bitbucket, etc.)Demonstrated expertise / experience in several of molecular AI/ML areas, such as generative chemistry; structure-based drug design across hit finding, hit to lead, lead optimization, and candidate selection; protein-ligand modelling, co-folding; docking, scoring, and pose assessment with rigorous model validation and tight coupling to experimental follow up; QSAR; multi-objective property optimization (uncertainty estimation, active learning concepts); free energy and affinity predictionDemonstrated expertise / experience with AI driven molecular design applied to the areas above· Experience working in a large Research organization & deep understanding of drug development a plusPassion for understanding emerging technologies with pragmatic insight into where those technologies can be integrated into business solutionsAbility to balance requirements, manage expectations, and drive effective results using a proactive attitude towards identifying and resolving issuesStrong organizational and problem-solving skills, with ability to execute and prioritize well in a complex matrixed environment. Working closely with multidisciplinary project teams, engineers, and other data scientists, the successful candidate will build and benchmark the right AI approaches, algorithms, models and workflows, to maximize impact on key domains areas of biomedical research that will potentially lead to developing better drugs, faster.