For our client, a leading company in the pharmaceutical sector, we are seeking a Computational Toxicologist to help our client develop innovative, AI-powered solutions that predict toxicological risks and translate data into actionable insights.
This is your opportunity to be part of a highly interdisciplinary team that bridges cheminformatics, data science, toxicology, and drug development. You will apply state-of-the-art machine learning to address critical safety questions and actively contribute to the integration of diverse data types—including chemical structures, in vitro assay data, and evolving omics readouts.
In addition to model development, you will work closely with discovery project teams to provide in silico safety assessments and scientific support. You’ll help reuse historical data to inform current programs and collaborate with colleagues across departments to identify pain points and develop impactful solutions.
General Information:
- Start date: ASAP
- Latest Start Date: September 2025
- Planned duration: 1 year
- Extension: not likely
- Workplace: Basel
- Workload: 100%
- Home Office: limited, 51% onsite minimum
- Working hours: Standard
Tasks & Responsibilities:
- Design, develop, and apply machine learning models to predict safety-relevant endpoints (e.g., liver or kidney toxicity) using chemical structure and biological data.
- Integrate chemoinformatics and in vitro safety data, with the potential to expand toward transcriptomics or other omics technologies.
- Provide in silico support for discovery and early development programs, offering scientific insights into potential safety risks.
- Leverage internal data and external knowledge bases to enhance model performance and interpretability.
- Collaborate closely with toxicologists, pharmacologists, data scientists, and chemists to co-create solutions and ensure models are meaningful and relevant.
- Contribute to broader efforts such as biological read-across, reverse translation of historical data, and refinement of digital workflows for safety decision-making.
Must Haves:
- PhD or MSc (with relevant experience) in Computational Toxicology, Cheminformatics, Bioinformatics, Data Science, Pharmacology, or a related field.
- Solid experience in developing machine learning models, ideally applied to chemical and biological data.
- Strong foundation in cheminformatics/chemistry, including working with molecular descriptors, chemical similarity, and structure-based analyses.
- Experience with toxicological datasets and safety endpoints such as DILI or nephrotoxicity.
- Familiarity with in vitro safety data and an interest in integrating complex biological datasets.
- Proficient in programming (e.g., Python, R) and using scientific computing libraries (e.g., RDKit, scikit-learn, Pandas, TensorFlow, or similar).
- Excellent communication and collaboration skills; able to translate technical insights for interdisciplinary teams.
Nice to Have:
- Experience with toxicological datasets and safety endpoints such as DILI or nephrotoxicity.
- Understanding of omics data integration or biological pathways related to toxicology.
- Familiarity with pharmaceutical R&D or prior experience in industry (a plus, but not essential).