Principal Computational Biologist...

Job Description

Our client, who is the largest Pharmaceutical Company in Europe is seeking a Principal Data Scientist. We’re partnering with their Data, AI, R&D team on a groundbreaking initiative: developing foundation models trained on cellular imaging data. One of their key models is built on half a petabyte of high-resolution cell imagery, aimed at transforming how we discover treatments for cardiovascular diseases, obesity, and rare conditions. We’re looking for a principal computational biologist or data scientist to play a critical role in this project — leading the analyse of image-based embeddings, validating model outputs, and collaborating closely with ML engineers and vision scientists to ensure the biology behind the model holds up.

Must Haves:

• PhD in Computational Biology, Bioinformatics, Systems Biology, or a related quantitative field.
  
• 5+ years of experience in computational biology or data science within the biotech or pharmaceutical industry.
  
• Proven track record in target discovery, with experience progressing targets through early drug development stages.
  
• Strong expertise in cellular imaging and high-content screening, including the analysis of microscopy images
  
• Hands-on experience integrating and analysing multi-modal biological data, such as transcriptomics, proteomics, or single-cell data.
  
• Proficiency in Python, including libraries such as Pandas, NumPy, scikit-learn
  
• Experience analysing deep learning model outputs, especially from vision models and working with biological image embeddings.
  
• Ability to evaluate model performance, interpret model outputs, and connect them to biological hypotheses.
  
• Demonstrated ability to drive independent analysis, design evaluation frameworks, and make data-driven recommendations in an R&D context.
  
  Plusses:
  
  
• Experience working with foundation models or self-supervised learning techniques applied to biological or image data.
  
• Familiarity with deep learning frameworks (e.g., PyTorch or TensorFlow).
  
• Familiarity with cell-level microscopy (e.g., Cell Painting, HCS) and downstream biological interpretation.
  
• Understanding of model feedback loops — such as collaborating with ML teams to refine models based on biological relevance.
  
• Strong grasp of data visualization tools and techniques for communicating multi-dimensional biological findings.
  
• Excellent communication skills, especially in translating ML/AI outputs into biological insight and decision-making.