Head of Machine Learning Research

At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where 'Health for all Hunger for none' is no longer a dream, but a real possibility. We're doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining 'impossible'. There are so many reasons to join us. If you're hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there's only one choice. Head of Machine Learning Research We are seeking a scientific and technical leader to advance machine learning–enabled molecular design across our drug discovery portfolio. As a member of the Data Science & AI leadership team, you will define and deliver the models, platforms, and ways of working that shorten design–make–test–analyze cycles and improve the quality of molecules progressing toward nomination. This is a hands-on strategic leadership role—neither research-only nor delivery-only. You will set a multi-year technical roadmap, build and lead a team of approximately 25 scientists and engineers, and ensure that machine learning capabilities translate into better project decisions and faster progression to candidate selection. You will lead the development and application of modern machine learning methods—including deep learning, neural networks, ensemble methods, natural language processing, and machine perception—to complex, high-dimensional scientific data. Your team will develop predictive and generative models that help discovery scientists understand biological and chemical patterns, prioritize experiments, optimize molecular properties, and design novel molecules. Success in this role requires deep fluency in contemporary machine learning, combined with a practical understanding of the realities of medicinal chemistry, structural biology, and drug discovery. You will spend as much time engaging in project-team reviews and scientific decision-making as you do shaping model architectures, platform strategy, and technical standardsYOUR TASKS AND RESPONSIBILITIESThe primary responsibilities of this role, Head of Machine Learning Research, are to: Strategy and Technical Direction Co-own the vision, roadmap, and investment case for ML-driven molecule design and multi-parameter optimization across small molecules and emerging modalities. Make build/buy/partner decisions across generative design, property prediction, structure-based ML, and synthesis planning; evaluate foundation models and external platforms with rigor rather than hype. Set the standard for how models are validated, benchmarked prospectively, and retired — including uncertainty quantification, applicability domain, and honest reporting of failure modes. Delivery into Discovery Projects Partner with Drug Discovery Sciences — medicinal chemistry, structural biology, biophysics, screening, DMPK, and safety — to embed ML into live programs from hit identification through candidate nomination. Co-own program-level design goals with chemistry leads: potency, selectivity, ADMET, developability, and IP position optimized together rather than sequentially. Drive active learning and closed-loop DMTA cycles, including integration with high-throughput and automated synthesis where available. Ensure models are delivered as reliable, supported products that chemists actually use, not one-off analyses. Platform and Data Foundation Direct development of core capabilities: ADMET and property predictors, generative and de novo design, retrosynthesis and synthetic accessibility, and structure-based ML including co-folding, pose prediction, docking rescoring, and ML-accelerated free energy methods. Work with data engineering to secure the assay, structural, and DMTA data foundation these models depend on — curation, provenance, harmonization across assays and sites, and feedback capture from every make-test cycle. Establish MLOps practice appropriate to a regulated R&D environment: versioning, reproducibility, monitoring, and documentation. Leadership and Organization Build, lead, and develop a team of approximately 25 spanning ML research, computational chemistry, and ML engineering; grow leaders within the group. Own budget, vendor relationships, and external collaborations with academic groups, consortia, and biotech partners. Represent the function to R&D leadership, translating technical capability into portfolio impact and translating portfolio priorities into technical strategy. Contribute to the broader scientific community through publication, presentation, and precompetitive collaboration where appropriate.