Job Description

Are you passionate about leveraging machine learning to make medicines safer and faster to develop? Our client in Basel is seeking a Computational Toxicologist to join a cross-functional team advancing AI-driven safety assessments for drug discovery. In this role, you will apply cutting-edge computational tools to predict toxicological outcomes, integrate diverse data types, and translate insights into actionable recommendations for early drug development. You will collaborate with experts across toxicology, data science, and cheminformatics to drive innovation in predictive safety.
Job Title: Computational Toxicologist
Department: Predictive Modelling (PNV)
Location: Basel, Switzerland
Workload: 100%, Hybrid possible
Start Date: As soon as possible (latest: 01.09.2025)
Key Responsibilities:
Design and implement machine learning models to predict toxicity endpoints (e.g., DILI, nephrotoxicity) using chemical and biological datasets.
Integrate cheminformatics with in vitro assay data, and potentially omics technologies, to enhance safety predictions.
Deliver in silico safety support to discovery programs by interpreting model outputs and contributing scientific insights.
Reuse and mine historical and external data to optimize model performance and usability.
Work closely with interdisciplinary teams to ensure solutions are scientifically sound, applicable, and impactful.
Contribute to broader initiatives such as biological read-across, reverse translation, and digital workflow enhancements.
Must-Have Qualifications:
PhD or MSc (with relevant experience) in Computational Toxicology, Cheminformatics, Bioinformatics, Pharmacology, or a related field.
Proven experience developing machine learning models applied to chemical and/or biological data.
Strong background in cheminformatics, including familiarity with molecular descriptors, chemical similarity, and structure-based analysis.
Hands-on experience with toxicological datasets and endpoints (e.g., liver or kidney toxicity).
Proficient in Python or R, and commonly used libraries (e.g., RDKit, scikit-learn, Pandas, TensorFlow).
Excellent collaboration and communication skills, with the ability to present complex data clearly to interdisciplinary teams.
Nice to Have:
Experience with omics data integration or biological pathway modeling in toxicology.
Familiarity with pharmaceutical R&D or previous experience in an industry setting.
Interest in expanding predictive models to novel data types and experimental readouts.
Sounds like a great job?
Then we look forward to receiving your complete application documents through our online application form.
When applying by email, the sender agrees that his or her data will be used in accordance with our data privacy policy.
Find more vacancies at:
Job profile

• Life Sciences - Data Science
• Basel-Stadt
• Contracting
• Full Time
• 01/09/2025 - 30/09/2026
• , Toxicology, GMP

Sounds interesting?
Simply click "Apply now" and I will get back to you.
Madalina Poenaru
Talent Acquisition Specialist
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