Associate Principal Scientist, Cheminformatics — Onsite

AstraZeneca GmbH; Mölndals kommun; On-site; SEK 900,000 - 1,200,000; Full time

Job summary

AstraZeneca GmbH in Gothenburg, Sweden seeks a Chemoinformatician (Associate Principal Scientist) to advance computational chemistry across discovery. You will work with machine learning, medicinal chemistry and multi-disciplinary teams to design data pipelines, predictive models, library design and SAR analysis to improve potency, selectivity and ADMET properties.

You will contribute to capability building, publish research, and collaborate with AZ sites and external partners, using HPC

Qualifications

• PhD (or equivalent) in computational chemistry, chemistry, pharmacology, or related field.
• Deep knowledge in ML and DL, esp. generative design, QSA/PR or reaction prediction.
• Strong experience applying ML/AI in drug discovery, esp. ligand-based design techniques.
• Knowledge of scientific computing and programming (Python, Perl, C, C++, Java, R); cheminformatics toolkits (RDKit, OpenEye); ML frameworks (scikit-learn, PyTorch, TensorFlow).
• Strong written and verbal communication skills and ability to collaborate in multidisciplinary project teams.
• Excellent time management and delivery focus.

Responsibilities

• Apply computational chemistry and AI to design, data pipelines, predictive models, library design and SAR analysis to improve potency, selectivity, activity and ADMET.
• Collaborate across AZ sites and with external partners; contribute to capability building and publish in peer‑reviewed journals.
• Work with a large community of computational chemists, train and deploy ML models on HPC clusters, and explore generative design and reaction predictions.

Skills

ML/DL in drug discovery

Ligand-based design

Programming & scientific computing

Education

PhD in computational chemistry or related field

Tools

RDKit

OpenEye

Python

C/C++

Java

Job description

AstraZeneca GmbH in Gothenburg, Sweden seeks a Chemoinformatician (Associate Principal Scientist) to advance computational chemistry across discovery. You will work with machine learning, medicinal chemistry and multi-disciplinary teams to design data pipelines, predictive models, library design and SAR analysis to improve potency, selectivity and ADMET properties.

You will contribute to capability building, publish research, and collaborate with AZ sites and external partners, using HPC