Director, Computational Biology & AI

Cerenome (Nasdaq: CNSY) is a clinical-stage central nervous system (CNS) oncology company focused on transforming how rare and severe brain and spinal cancers are diagnosed, monitored, and treated. Our work brings together advanced diagnostics, targeted radiotherapeutics, and data analytics to address some of the most difficult challenges in neuro-oncology.

Our portfolio includes CNSide®, our proprietary cerebrospinal fluid-based diagnostic platform designed to support the diagnosis and management of cancers involving the central nervous system, and REYOBIQ™, our investigational radio therapeutic currently in clinical development.

As we grow, many of the systems, capabilities, and ways of working that will support our next stage are being built now. We see that as an opportunity—and we're looking for people who do, too.

Cerenome is looking for a Director, Computational Biology & AI to help establish and lead our internal computational biology and artificial intelligence capabilities. As our clinical and molecular data programs expand, this role will play a key part in translating complex scientific data into meaningful insights that support research, clinical development, and strategic decision-making.

This is a hands-on scientific leadership role for someone with deep expertise in cancer biology, computational methods, and advanced analytics. You'll help shape our scientific and data strategy, lead computational analyses, guide external technical partners, and establish the infrastructure and analytical approaches needed to support our next stage of growth.

We're looking for someone who can operate at both the strategic and tactical levels—setting scientific direction and advising senior leadership while also rolling up their sleeves to analyze complex biological data, develop computational models, challenge scientific assumptions, and translate findings into actionable recommendations.

As our organization grows, this role is expected to evolve and may include responsibility for building and leading an internal computational biology and AI team.

• Establish and lead computational biology strategy, scientific priorities, analytical approaches, and execution plans across four strategic programs involving treatment outcomes, molecular diagnostics, disease modeling, and therapeutic discovery.
• Design and conduct computational analyses of genomic, molecular, imaging, dosimetry, and clinical outcomes data using statistical modeling, machine learning, and advanced analytical methods.
• Build scalable scientific data capabilities, including data infrastructure, standards, analytical workflows, and governance practices that support reliable and reproducible research.
• Identify and implement practical AI and machine-learning applications that strengthen scientific research, clinical development, and data-driven decision-making.
• Establish standards for scientific rigor, including model validation, reproducibility, data quality, documentation, and regulatory readiness.
• Provide scientific direction and oversight to external data engineering partners, technical consultants, academic collaborators, and other research partners.
• Partner across Clinical Operations, Diagnostics, Regulatory, Quality, Biostatistics, and scientific leadership to translate clinical and business questions into meaningful analytical initiatives.
• Develop computational models and analytical approaches that help identify scientific trends, evaluate treatment outcomes, and support translational research.
• Help define future organizational capabilities and talent needs, including recruiting, mentoring, and developing technical specialists as the function grows.
• Present scientific analyses, findings, recommendations, and program…