Senior Expert II Data Scientist

Major accountabilities:

• Serve as a trusted partner between Data & Digital (D&D) and PKS wet and dry lab teams to identify gaps and translate business needs into strategically aligned solutions within the D&D portfolio. 

• Act as a data science representative in Integrated Drug Discovery (IDD) programs, providing scientific and strategic input using experimental and computational data. 

• Design, build, and maintain scalable data pipelines, applications, and analytical workflows. 

• Develop, deploy, and maintain machine learning models to uncover structure–property relationships and support decision-making. 
• Apply statistical analysis and data mining techniques to derive insights from complex biological and chemical datasets. 

• Write production-quality, maintainable code following software engineering best practices (testing, version control, documentation). 

• Collaborate across cross-functional teams to integrate computational solutions into scientific workflows. 

• Identify opportunities for automation, improved data usage, and development of in silico models and digital tools. 

• Communicate findings clearly to diverse audiences and contribute to the adoption of data-driven approaches. 

Minimum requirement

• PhD in life sciences, computational biology, cheminformatics, bioinformatics, or a related field, and 3-4 years (PhD) / 7-8 overall years of relevant work experience in drug discovery within biomedical or pharmaceutical research settings. 

• Strong expertise in machine learning, statistics, and data science workflows applied to drug discovery. 

• Proficiency in Python and/or R with solid software engineering practices. 

• Experience designing and deploying production-grade data products or ML systems. 

• Strong understanding of data visualization and exploratory analysis. 

• Excellent communication skills and ability to translate complex concepts into actionable insights. 
• Experience in pharmacokinetics (PK), ADME, or PK/PD modeling. 

• Familiarity with modern application frameworks or front-end technologies (e.g., JavaScript, Svelte). 

• Experience with advanced ML methods such as deep learning or generative models. 

• Experience working with large-scale scientific datasets and data platforms.