Develop ML/AI models that support target prioritization, multi-omics integration, and mechanistic inference.
Apply modern ML approaches (e.g., deep learning, graph learning, foundation models, generative models) to chemical, biological, imaging, and assay datasets.
Build and optimize models for real-world R&D use cases, ensuring scalability, interpretability, and scientific rigor.
Design, build, and maintain robust data pipelines that curate, standardize, and integrate diverse R&D datasets (chemical, biological, multi-omics, imaging, biophysical, automation logs, etc.).
Partner with platform teams to implement best-practice MLOps/DevOps workflows and deploy ML models into production R&D environments
Develop tooling that accelerates dataset preparation, feature engineering, and model lifecycle management.
Work hand-in-hand with discovery scientists to understand key biological and chemical questions and shape computational strategy accordingly.
Translate sparse, heterogeneous experimental datasets into insights that guide decision-making in hit discovery, mechanism studies, perturbation experiments, and compound optimization.
Participate in design, interpretation, and iterative refinement of discovery experiments.
Partner with cross-functional teams in R&D Data Science, IT, platform engineering, and therapeutic area groups to drive AI/ML adoption.
Contribute to evaluating new analytical methods, automation technologies, and data platforms supporting next-generation discovery science.
Promote high standards for data quality, documentation, governance, and reproducibility.
Requirements
Master’s or Ph.D. in Computational Biology, Bioinformatics, Data Science, Chemistry, Chemical Biology, Biomedical Engineering, Computer Science, or related fields
Experience applying ML/AI in scientific domains (drug discovery, biology, chemistry, systems biology, imaging, or related areas)
Strong Python skills, with experience in scientific and ML libraries (PyTorch, TensorFlow, scikit-learn, RDKit, etc.)
Practical experience with data engineering, including data modeling, workflow orchestration, ETL/ELT pipelines, and cloud computing environments (AWS, GCP, or Azure)
Ability to work directly with experimental scientists to solve real R&D challenges
Strong communication skills and ability to thrive in a matrixed, multidisciplinary environment
English level B2+ or higher
Nice to have
Experience in pharma or biotech discovery, including target assessment, phenotypic screening, medicinal chemistry workflows, and lab automation
Familiarity with omics, high-content imaging, chemical structure data, or biological assay data
Knowledge of data standards (e.g., FAIR, ontologies, controlled vocabularies) and working within regulated or quality-governed environments
We offer
Competitive compensation
Remote or office work
Flexible working hours
Healthcare benefits: medical insurance and paid sick leave
Continuous education, mentoring, and professional development programs
