Sander Timmer, PhD

Head of Data Science and AI for Global Drug Development at Novartis, leading the strategic integration of AI to accelerate how we reimagine medicine for patients. Based in Basel, I focus on fundamentally changing the speed and precision of drug development from candidate optimization and clinical trials through CMC to regulatory submission.

Pivoted to a Forward Deployed AI Engineering operating model, embedding AI/ML and Decision Sciences teams directly within Global Supply Chain operations. Delivered £XXXM in validated business value through AI solutions at the point of decision-making, ensuring measurable value realization and operational integration. Key Responsibilities & Impact - Forward Deployed Model: Established forward-deployed engineering teams embedded with GSC operations, delivering AI solutions at the point of decision-making. -Built unified AI Ops platform for GSC, shifting from KPI dashboards to rigorous AI value realization with full observability across 300+ solutions, 400k+ industrialized AI models, and 100+ multi-agent systems. - Physical AI & Robotics: Leading deployment of Physical AI and robotics for GMP automation, integrating embodied agents with digital twin systems. - Pioneering Vision Language Models for real-time quality inspection and visual decision-making in manufacturing through autonomous agentic workflows. - BI to AI/BI Evolution: Driving transformation from traditional BI to Conversational AI and Agentic BI (PowerBI Copilots, Copilot Studio). - Overseeing GSC AI Council; validated 300+ built and bought solutions for EU AI Act/FDA compliance. Key Presentations - Fully Connected London (Nov 2025): "GSK AI Vision" – Unified governance framework for 300+ solutions and physical AI systems.

Founded & scaled AI CoE from 0 to 40+ FTEs +200 consultants (US/UK/EU/Poland/India/Singapore), delivering GxP AI for CMC/manuf./quality: 10+ multi-agents (50M inferences/yr), digital twins (+20% yield/12 assets, +1M doses), copilots (80% savings). Secured £25M funding; partners: Nvidia, Databricks, MSFT, Google. Key Solutions & Impacts - Team/Org Build: Founded global CoE; scaled via strategic hiring and large graduate and intern programs. - Digital Twins (process/network): Cloud-edge IoT/RL for GMP robotics (+1M vaccine doses). - Agents/Copilots: GenAI/LLMs (Llama/Qwen fine-tunes) for predictions, text/vision. - Platforms: MLOps/LLMOps, Process Mining, Feature Store, ONNX. - GSK Platforms: TwinOps (twins), AIGA (GenAI)—GxP/enterprise robust. - AI Governance: Led GSC council, validated 200+ solutions (EU AI Act/FDA). - BI and Automation Evolution: Traditional BI to AI/BI with conversational AI, PowerBI copilots, Copilot Studio. Key Presentations - Databricks Data+AI Summit (Jun 2024, SF): "Harnessing GenAI & Digital Twins in Pharma Manufacturing" – TwinOps Copilot, synthetic data for GxP. ​- PAIS 2024: "Improving Yield via Digital Twins" (Last Author). ​

Founded GSK Vaccines' first AI division (10 FTEs +40 consultants, US/EU/India), pioneering R&D AI for target discovery to Phase 3 trials: protein language models accelerating reverse vaccinology (novel targets validated in vitro), first Digital Twin for clinical trial endpoint prediction, document automation via BERT-based NLP. Built cloud-native/edge Data Products with MLOps. Teams own Data Products in: - Target Discovery: Protein LMs + multi-omics/cryo-EM AI streamlining early vaccine design. - Clinical Twins: GSK's inaugural Phase 3 endpoint predictor, enhancing trial efficiency. - Document Automation: BERT/NLP for regulatory text generation, inspections, protocol analysis. - Tech Stack: Vision, AutoML, Feature Store, ONNX in GxP environments.

In this key Health and Life Sciences role, I'm responsible for providing academic guidance, thought leadership, vision, and strategy for Microsoft technologies and solutions to impact patient outcomes. I work with pharmaceutical companies and healthcare organisations around the world, but mainly EMEA, to improve and transform health outcomes by leveraging machine learning and deep learning solutions in the cloud. Key areas of interest: - Digital Biomarkers - AI driven Diagnostic tools - Precision medicine by AI methods on top of EHR and Omics data - Smarter reuse of (Connected) Clinical trial data - Digital medical assistants - Pharmacogenomics and other multi-omics scenarios - Real World Evidence (RWE)

Scoping and designing analytic solutions featuring Bots, AI, IoT, Deep Learning and Machine Learning. Responsible for data science outcome in project teams of consultants, business architects, and solution architects during pre-sales and delivery. Using Microsoft Azure cloud technologies to drive digital transformation at Microsoft' largest customers.

Working in the Data Insights CoE (Center of Excellence) for our EMEA enterprise customers. Consulting customers on making advanced analytic solutions for their Big Data problems with a strong emphasis on integrating these solutions as an end-to-end business solution. Building cloud based Machine Learning and Internet-of-Things (IoT) solutions on themes like Predictive Maintenance, Churn Analyses, Stock Optimisation, Sentiment Analyses, and Market Segmentations.

Utilisation of the Microsoft Azure cloud to solve Big Data problems using Machine Learning. Data integration of private and public open data. Realtime analytics for Internet of Things (IoT) business cases. Consultant Data Scientist in the Western Europe Consulting Practice (WECP). MACH hire 2015

Flexible search engine create comparisons using location, aggregated exam statistics and local user reviews. Largest driving school comparison website with the most actual user experiences. Delivering web technologies to driving schools in the Netherlands. Flexible search engine create comparisons using location, aggregated exam statistics and local user reviews. Largest driving school comparison website with the most actual user experiences. Delivering web technologies to driving schools in the Netherlands. https://www.rijschoolvergelijker.nl

Big data biology: integrating large-scale epigenetic and genetic data. Utilising the EBI computational cluster to reconstruct the human epigenome and its genetic regulation. Statistical analysis of data sets on a terabyte scale. Thesis work was published in a first-author paper in PLoS Genetics. Side project: measure multiple phenotypes in human MRI scans using machine-learning approaches (HMM and 3D probability atlases).

Big data biology: integrating large-scale epigenetic and genetic data. Utilising the EBI computational cluster to reconstruct the human epigenome and its genetic regulation. Statistical analysis of data sets on a terabyte scale. Thesis work was published in a first-author paper in PLoS Genetics. Side project: measure multiple phenotypes in human MRI scans using machine-learning approaches (HMM and 3D probability atlases). During my PhD focus has been on the following projects: - Reconstructing the Human Epigenome using therabytes of sequencing data. Integration of chromatin status (FAIRE-chip), transcription factor binding (CTCF-seq),gene expression (RNA-seq), and genetic variation (DNA-seq). Used System Genetics approaches to discover distinct causality within the heap of correlations within in epigenome. - Novel patterns of CTCF binding at the X chromosome are observed in relation to X-chromosome inactivation. These patterns are described and a statistical model has been build to discover three distinct binding patterns. - Applied machine learning techniques (e.g. HMM and probability atlases) to automatically measure skeletal phenotypes in MRI scans.

Working with Next-generation DNA sequencing data from the 1000 genomes project. Using the low-coverage samples studied the evolutionary constraints of indels.

Biomarker discovery in cancer proteomics data. Developed biomarker discovery pipeline for large-scale cancer proteomics data. Pipeline was used to identify known and novel prostate cancer biomarkers in serum.

Technical support and development on several projects using techniques like: - Create Database Procedure (in MSSQL and MySQL) - implementing innovation web technologies (ASP & PHP & Perl)

Developed modules for the in house written CMS including: - iDEAL payments - social activities (forum)

Graduate student at Clare Hall

Minor project: The influence of protein dynamics on structural alignments (supervised by Walter Pirovano, Anton Feensta and Jaap Heringa) Optional master course: Advanced Bioinformatics (12 ECTS) Academic Medical Center and University of Amsterdam by Prof. Dr. A.H.C. van Kampen and Dr. Ir. P.D. Moerland. Central in this course is the statistical program R and statistical research on cancer data and genomic data. Biomolecular Mass Spectrometry course 2008 Utrecht University (Netherlands Proteomics Centre) by Prof. Dr. Albert Heck 22-09-2008 - 26-09-2008