Define and lead the applied artificial intelligence strategy and multi-year roadmap across drug discovery research. Align priorities with portfolio needs, scientific opportunities, and measurable business and research impact. Lead multidisciplinary teams to identify, prototype, benchmark, and deploy fit-for-purpose artificial intelligence solutions. Govern an applied artificial intelligence portfolio with clear intake, prioritization, resourcing, delivery oversight, and success metrics. Establish best practices for problem framing, data readiness, benchmarking, evaluation design, and reproducible model development. Drive benchmarking of foundation and task-specific models, enabling transparent trade-offs and informed adoption decisions. Partner with engineering teams to scale solutions and embed them into day-to-day scientific decision making. Define rigorous evaluation metrics linking model performance to downstream decisions and experimental outcomes. Build a culture of scientific rigor, rapid iteration, mentorship, and practical impact across teams. Forge strategic academic and industry collaborations to accelerate innovation, benchmarking, and technology transfer. Essential Requirements. Demonstrated experience in leading core machine learning capability development initiatives across drug discovery teams and use cases. Proven experience with foundation model benchmarking in drug discovery applications. Hands-on experience applying machine learning to core drug discovery areas such as target identification or computational chemistry. Strong experience in large-scale model training, distributed computation, model adaptation, and deployment within machine learning operations frameworks. Deep curiosity and passion for biomedical sciences and therapeutic discovery, with ability to explain complex technical concepts clearly. Minimum of 12 years of experience in innovation, development, deployment, and continuous support of machine learning and modeling solutions. Strong coding proficiency in Python and deep learning frameworks, with experience using version control systems such as Git. Ability to manage complexity, balance priorities, and drive outcomes effectively within matrixed environments using a proactive mindset. Desirable Requirements. Publications, patents, or open-source contributions demonstrating machine learning innovation and domain expertise. Strong curiosity for emerging technologies with pragmatic ability to apply them to real-world business challenges. Compensation and Benefits.