About the job
About Us
At Verge Genomics, we are revolutionizing the drug discovery process by harnessing the power of artificial intelligence and a rich database of human data to tackle the major factors contributing to escalating drug costs—particularly high clinical failure rates. We have established one of the industry's largest collections of multi-modal patient molecular and clinical data, obtained directly from human tissue samples. Our talented team, comprising engineers, neuroscientists, and biologists, has successfully brought two drugs to clinical trials, identified 282 new therapeutic targets, and forged commercial partnerships exceeding $1.6 billion with major pharmaceutical companies such as Eli Lilly and AstraZeneca.
Your Role
As the Principal/Director of Computational Biology, you will report directly to the Head of Product & Engineering. Collaborating closely with Verge’s leadership and technical teams, your key responsibility will be to spearhead the Computational Biology initiatives, delivering valuable insights to our biopharma partners and clients that will inform their research and development strategies.
Your Key Achievements in the Next 12 Months
Produce two comprehensive insights reports (e.g., target characterization) for biopharma partners that will enhance existing partnerships.
Innovate new methodologies to support product development, including patient stratification and biomarker discovery.
Assess and benchmark the company's foundational models for translational tasks, aiming for superior performance compared to current standards.
Collaborate with the Head of Business Development to expand Verge's proprietary datasets.
Your Responsibilities
Derive biological insights regarding therapeutic candidates and disease biology through the analysis of diverse multi-modal datasets (including transcriptomics, genetics, and clinical data).
Translate overarching business goals into actionable scientific work plans.
Integrate various datasets to formulate and test hypotheses for target prioritization, mechanism-of-action elucidation, indication expansion, repositioning, biomarker identification, and patient enrichment.
Communicate findings effectively to external stakeholders to influence pharmacological R&D decisions.
Develop and validate metrics to assess the efficacy of AI models in translational applications.
Interpret and analyze outputs generated by AI foundational models.
Bridge the gap between biological insights and machine learning objectives.