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
Experience & Qualifications
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
Benefits
Performance-based bonus
Flexible working hours
Nice to Have
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
Skills
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