Biberach an der Riß, Baden-Württemberg, Germany
Strategic Lead: Computational Biology & Translational AI | Architect of Multi-Omics & RWE Integration | R&D Innovation Leader I bridge the gap between high-dimensional biological data and drug R&D strategy. As a Principal Scientist and the NCS Germany Digital Lead at Boehringer Ingelheim, I have been the primary architect for transforming how my department utilizes data to drive R&D outcomes. My core strength lies in the dual mastery of Innovation and Execution: I have a proven track record of creating novel computational approaches from scratch and scaling them into robust, department-wide frameworks. I don’t just apply existing tools; I build the foundational infrastructure and multi-modal profiling capabilities that make advanced, high-scale analytics possible. Key Impacts & Pioneering Leadership: * Infrastructure Architect: I established the first multi-modal profiling capabilities in my department, specifically architecting the integration of Real World Evidence (RWE) with high-dimensional Omics data. Before my tenure, these integrated capabilities did not exist; I built the foundational architecture, data roadmaps, and workflows from the ground up. * Strategic Bridge (Non-Clinical to RWE): I serve as the critical link between raw omics data and RWE. By architecting this foundational infrastructure, I enable the seamless translation of non-clinical findings into real-world clinical contexts, improving R&D outcomes and mechanistic understanding. * AI & Foundational Models: Beyond the pioneering adoption of the STATE virtual cell model, I bring deep know-how in a broad range of Machine Learning, Deep Learning, and Foundational Models tailored for mechanistic modeling and risk assessment. * Scaling Innovation: I lead the end-to-end lifecycle of computational innovation, from conceiving novel multi-omics methodologies to scaling them for industrial-level production and standardized analysis. * Cross-Functional Catalyst: I set the stage for large-scale collaboration, aligning multi-modal analytics and computational innovation with strategic business goals while significantly raising data literacy across the organization. I am a frequent contributor to the global scientific community and was recently invited as expert panelist for FAIR and data integration and AI in model informed drug development at BiotechX 2026 Core Competencies: Creating & Scaling Novel Computational Approaches, Multi-modal & Multi-omics Integration (RWE/Omics), Systems Biology, Artificial Intelligence (AI/ML), and Digital Transformation Leadership.
Serving as the Global Lead Expert in Systems Toxicology and Computational Sciences, operationalizing the departmental strategy for high-scale multi-modal data integration. Driving the long-term innovation strategy for computational methods and AI-driven tools to enhance the predictive power of non-clinical safety. Acting as a bridge between scientific innovation and drug safety strategy, ensuring cutting-edge computational models are integrated into the core R&D decision-making process. Providing expert oversight for the integration of diverse data streams to improve mechanistic understanding and translational R&D outcomes.
Lead Systems Toxicology Team | Digital Lead Boehringer Ingelheim | 04/2024 – 08/2025 • Operational Excellence: Orchestrated the digital transformation of NCS Germany by delivering a robust F.A.I.R. data management system, successfully migrating the department from manual processes to a high-speed, integrated digital environment. • Scaling & Efficiency: Directed a multidisciplinary team of 5 scientists to industrialize multi-modal analysis, realizing a 30% improvement in turnaround time and accelerating data-to-decision cycles for the global portfolio. • Strategic AI & Foundational Models: Led the large-scale industrial execution of Generative AI and Foundational Models for predictive toxicology, transforming advanced computational theories into production-ready tools for mechanistic risk assessment. • Cross-Functional Integration: Architected the bridge between Real-World Evidence (RWE) and non-clinical data streams, ensuring that AI-driven insights were validated against clinical reality to improve drug safety outcomes.
• Pioneering Capability Engineering: Identified a critical organizational gap and engineered the department’s first internal multi-modal profiling infrastructure from the ground up, moving the organization from zero to a fully integrated data environment. • Architectural Ownership: Designed and launched the strategic roadmap for high-scale omics analytics, transforming fragmented data into a standardized, high-value corporate asset for drug discovery. • Operationalizing Data Strategy: Facilitated a department-wide shift to a data-centric culture by delivering the actual computational frameworks and technical training that enabled bench scientists to adopt data-driven research. • Cross-Functional Product Delivery: Led the end-to-end development of in-house bioinformatics tools, ensuring that complex multi-modal integrations were not just theoretical but functionally available for pipeline decision-making.
• Standardization Leadership: Engineered and deployed a unified computational pipeline for standardized scRNA-seq analysis, migrating the department from fragmented scripts to a production-grade workflow. • Strategic Data Asset Management: Led the curation and "FAIRification" of large-scale public and internal datasets to accelerate Cancer Immunotherapy research.
Capgemini (On-site at Roche) | 05/2020 – 2021 • Project Delivery: Directed the "End-to-End Single-cell Analytics" pilot project, overseeing technical execution and cross-functional timelines. • HPC Optimization: Collaborated on high-performance computing (HPC) environments to optimize large-scale omics processing.
• Methodological Innovation: Developed a novel bioinformatics pipeline for transcriptomics-based mutational analysis, focusing on carcinogen-specific patterns. • Infrastructure Design: Architected the lab's IT and database infrastructure to support advanced analytics and F.A.I.R. data principles.