Data Scientist / PostDoc - Machine Learning & Profile - Driven Enzyme Discovery

Job description

Zur Jobsuche Startseite Stellenangebot: Data Scientist / PostDoc - Machine Learning & Profile - Driven Enzyme Discovery bei Bayer AG Pharmaceuticals Das Wichtigste im Überblick Besondere Merkmale Praktikum/Trainee/Werkstudent Beginn ab 01.11.2026 Arbeitsort Monheim am Rhein Anstellungsart Vollzeit Berufsbezeichnung Machine Learning Engineer Info zur Bewerbung Vormerken Notiz / Status Notiz hinzufügen Bitte anmelden, um diese Funktion zu nutzen. Bewerbungsstatus hinzufügen Bitte anmelden, um diese Funktion zu nutzen. PDF / Drucken Teilen Veröffentlichungsdatum: Vor 22 Tagen veröffentlicht Änderungsdatum: Vor 2 Tagen bearbeitet Stellenbeschreibung 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. Data Scientist / PostDoc - Machine Learning & Profile - Driven Enzyme Discovery We are seeking a highly skilled and motivated Research Scientist to join our interdisciplinary R&D team in Monheim, Germany, focused on machine learning-driven discovery and profile-driven enzyme design. In this role, you will help advance industrial protein design by connecting probabilistic modeling, sequence-to-function predictions, design of experiments, and active learning to unlock novel protein variants for high-impact pipeline applications. The successful applicant will work in a cross-divisional environment across Pharma and Crop Science R&D, contributing to state-of-the-art research while translating complex computational approaches into practical scientific impact. YOUR TASKS AND RESPONSIBILITIES • Develop and deploy active learning loops and probabilistic optimization strategies to explore vast, unknown sequence spaces and drive the discovery of highly informative variants for lab testing. • Build multi-objective optimization models capable of discovering entirely new enzymes that simultaneously meet complex performance profiles. • Implement predictive machine learning models, leveraging state-of-the-art protein representation learning to capture deep sequence-structure-function relationships and uncover novel biological insights. • Collaborate closely with scientific data experts to leverage complex knowledge graphs, and work with wet-lab scientists to evaluate model-generated hypotheses and interpret Design of Experiments (DoE) results. • Drive methodological innovation and translate highly complex probabilistic models and algorithmic discoveries into clear business impacts, risk assessments, and R&D strategies for executive leadership. WHO YOU ARE • You hold a PhD in machine learning, computational biology, physics, mathematics, or a highly quantitative discipline. • You bring deep theoretical and practical expertise in advanced machine learning, specifically probabilistic modeling, optimization algorithms, and active learning strategies geared towards scientific discovery. • You have a solid grasp of protein chemistry and mutational effects, ensuring that ML-generated predictions are biologically plausible and translate into actionable discoveries for the wet lab. • You actively challenge the status quo, relentlessly pursuing methodological innovation to solve complex, noisy biological problems and uncover new mechanisms in novel ways. • You possess strong collaboration strategies, successfully orchestrating the "Closed Loop" process by seamlessly bridging algorithmic hypothesis generation, wet-lab execution, and model refinement. • You are proficient in modern programming languages and the standard ecosystems for deep learning and probabilistic modeling. • You communicate clearly in English, both verbally and in writing, and can distill complex probabilistic concepts and scientific discoveries into strategic insights for cross-functional teams and leadership. Position Context This two-year limited position is firmly embedded within the Bayer Life Science Collaboration (LSC) framework—Bayer’s premier cross-divisional innovation platform. Operating specifically within this framework over a defined 24-month timeline, you will leverage its unique ecosystem to drive breakthrough R&D innovation through scientific collaboration, knowledge exchange, and rapid experimentation across Pharmaceuticals, Crop Science, and Consumer Health. The LSC framework is designed to bring together diverse talents and disciplines. By working within this collaborative structure, you will have the platform and resources to address strategic R&D challenges, develop pipeline-enabling solutions, and accelerate the translation of novel,