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Ready to push the boundaries of drug discovery? This is your chance to join a cutting-edge biotech shaping the future with condensate biology—an entirely new frontier in human health. Looking for a visionary Associate Principal / Principal Computational Biologist to harness advanced analytics, AI, and biological insight to crack the toughest therapeutic challenges. If you live for big data, bold ideas, and impact, this is the role that lets you change the game.

Our client is a fast-growing, science-driven biotechnology company transforming the treatment landscape through cutting-edge condensate science. Leveraging a unique end-to-end platform, they integrate high-resolution biology, AI, and machine learning to accelerate drug discovery and develop novel therapies for complex diseases where current options fall short. With strong backing from leading investors, a culture of innovation and inclusion, and an inspiring mission to address urgent patient needs, this is a truly exciting place to advance your career.

Responsibilities/Role In this influential role, you will work at the interface of data, algorithms, and biology, driving insights to accelerate therapeutic programs:

• Design and implement computational strategies for analyzing large-scale multi-modal biological data
• Develop models to elucidate mechanisms of disease and compound action
• Partner with experimental biologists and chemists to deliver data-driven hypotheses
• Contribute to the development of new platform tools for condensate biology

• Advanced degree (Ph.D. or equivalent) in Computational Biology, Bioinformatics, Systems Biology, or related field
• Proven experience in computational modeling, algorithm development, and analysis of multi-omics datasets
• Broad understanding of molecular biology and drug discovery

• Performance-based bonus
• Flexible working hours

• Exposure to AI/ML pipelines within a biopharma environment
• Knowledge of graph theory, network biology, or systems pharmacology
• Experience with computational chemistry or physics-based modeling

• Expertise in R, Python, or similar programming languages
• Proven skills in statistical modeling, machine learning, and data visualization
• Experience with NGS, proteomics, and structural biology datasets
• Familiarity with cloud computing and scalable data pipelines