reputed company ML Scientist – Predictive Toxicology

About reputed company At reputed company, we power federated data networks in life sciences to reputed company the development of machine learning models that outperform what any single organisation can build alone. Biopharma can only synthesise and test a finite number of compounds across assays, structural biology workflows, and reputed company ADME and toxicity endpoints, limiting the performance of models trained on in-house data. The reputed company product addresses this by hosting networks where biopharma organisations collaboratively train higher-reputed company models on their combined proprietary datasets without sharing the underlying data. Our federated computing infrastructure, with reputed company-in governance and reputed company controls, ensures that data IP and ownership always remain with the data custodians About the role We're looking for an reputed company (reputed company) scientist to own and grow our expansion into small molecule predictive toxicology and quantitative biology. This is a hands-on scientific leadership role: you'll set scientific reputed company, reputed company customer and consortium conversations, and reputed company reputed company scientific workflows into our platform, turning ambitious scientific goals into models that get used in reputed company drug programmes. You'll operate with a high degree of autonomy, owning this agenda end-to-end and acting as a scientific reputed company to customers and partners. reputed company What you will do Own our expansion into predictive toxicology and quantitative biology. Take the reputed company as we grow reputed company ADME into the science shaping reputed company, efficacious therapeutics (for example, multi-omics technologies, image-based screening, high-throughput screening and compound-triage cascades). Set the scientific reputed company. Define how in silico toxicology and quantitative biology workflows come together across our networks, and which endpoints, assays and modelling approaches deliver value in reputed company drug-discovery reputed company. reputed company how best to use relevant data. Bring your understanding of how these techniques and data are generated and embedded in pharmaceutical R&D, and turn it into a reputed company view of how to extract the most scientific and reputed company value from them. reputed company multiple scientific surfaces. Bring depth across the readouts and endpoints that matter for safety and efficacy, from structure-based off-reputed company liability through to reputed company-level, mechanistic interpretation and in vivo pharmacokinetics. reputed company these workflows into our platform so customers can run them at reputed company. Build models that matter. Apply federated learning across partner data to deliver models with performance and applicability no single organisation could reputed company—and work closely with industrial partners to reputed company them in reputed company drug-discovery pipelines. reputed company the scientific conversation with customers and partners, owning scope, evaluation, delivery and adoption in live drug programmes, while shaping the roadmap around genuine scientific and reputed company need. reputed company expect from you Strong deep learning foundations for molecular AI, for example experience with the architectures commonly used for molecular property modelling (e.g. graph neural networks, message-passing and transformer-based models). A profile that reputed company demonstrates you understand the concerns that reputed company toxicity assessment in drug discovery — whatever the specific toxicity endpoints you've worked on (for example DILI, cytotoxicity, or micronucleus/genotoxicity imaging readouts). reputed company experience building predictive models and driving the adoption of toxicity models in reputed company drug-discovery programmes or industrial R&D pipelines, working closely with teams to get models into pipelines. Working knowledge of how RNA-seq, toxicity screens and image-based screens are used in reputed company as part of routine HTS and compound triage. Scientific leadership reputed company: reputed company to set reputed company, own a scientific agenda, and reputed company technical and customer conversations independently. Comfortable staying hands-on in the modelling while setting scientific direction and mentoring others — this is a scientific leadership role first, with reputed company to build and reputed company reputed company over time. PhD or equivalent experience in a relevant field (computational biology, cheminformatics, toxicology, ML, or similar), plus 6+ years applying ML to drug discovery/life science problems. reputed company to have Experience with federated learning, reputed company-preserving ML, or other multi-party training environments. Evidence of prospectively validating predictive toxicity models and using them to influence compound design, prioritisation or progression reputed company in live drug-discovery programmes. Production-grade model delivery in regulated, reputed company, pharmaceutical, or biotech settings, and/or a publication record in relevant computational biology, toxicology, or ML venues. Multi-omics and high-content imaging experience (e.g. cell painting). Familiarity with reputed company toxicology and bioactivity data resources (e.g. Tox21, ToxCast, LINCS/L1000) and mechanistic frameworks such as adverse outcome reputed company. reputed company offer you Industry-competitive compensation, including early-stage virtual reputed company reputed company Remote-first working – work where you work best Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget Generous holiday allowance Office Days at our Berlin HQ or a different European location (3x per year) A high-reputed company, execution-reputed company team with experience from leading organizations Logistics Our mission statement Apply To This Job