Scientific Strategy, Oncology

About the role

Ataraxis AI is expanding its clinical research efforts to new cancer indications beyond our existing breast cancer platform. You will spearhead the scientific development and launch strategy for these new disease areas by bridging the gap between complex clinical data and our proprietary AI models. In this capacity, you will own the identification and validation of novel oncology opportunities that align with our technical capabilities and commercial objectives. You will act as the primary scientific architect, translating ambiguous clinical needs into structured research programs and executable product roadmaps. The role requires you to synthesize complex biological and clinical evidence into coherent narratives that guide both internal decision-making and external stakeholder engagement. You will be accountable for delivering a clear, data-driven rationale for each new indication pursued, ensuring scientific rigor is maintained at every stage. By doing so, you will define the scientific trajectory of our oncology portfolio and establish the foundation for sustainable growth in these new markets.

Key facts

What you'll do

• Lead the end-to-end expansion into new cancer types, managing the process from initial scientific research to product launch while maintaining strict adherence to timelines and quality benchmarks.
• Cultivate and maintain a robust network of clinical advisors, research partners, and physician leaders to ensure continuous insight into evolving treatment paradigms and unmet needs.
• Partner with the data team to design and execute strategies for acquiring, curating, and refining the high-dimensional datasets essential for model development and validation.
• Liaise closely with engineering and research staff to translate scientific requirements into technical specifications that facilitate the deployment of new clinical models for specific oncology indications.
• Define and own the scientific narrative for each new cancer area, creating compelling evidence bases that support strategic decisions and resource allocation.
• Establish and manage rigorous literature and data review processes to identify emerging trends, target vulnerabilities, and potential therapeutic synergies within the oncology landscape.
• Develop and present comprehensive analyses of clinical trial landscapes, including enrollment patterns, endpoint selection, and competitive positioning for prospective programs.
• Translate complex trial data and real-world evidence into accessible formats that resonate with both technical and non-technical audiences across the organization.
• Drive the creation of scientific deliverables such as indication dossiers, model performance reports, and strategic briefing documents that underpin go-to-market planning.
• Ensure that all scientific outputs reflect a deep understanding of clinical workflows, regulatory considerations, and the practical realities of oncologic practice.
• Build and document standard operating procedures for evidence assessment, model evaluation, and hypothesis generation to institutionalize best practices.
• Act as the central scientific hub for oncology initiatives, coordinating cross-functional input to maintain alignment between data science, clinical strategy, and product development.

Requirements

• Hold a PhD or MS degree with a documented history of high-quality research and peer-reviewed publications in relevant fields.
• Demonstrate the ability to engage with medical oncologists as a peer when discussing clinical studies, translational science, and intricate drug trial methodologies.
• Exhibit a proven track record of high initiative and self-directed project management, consistently delivering results with minimal direct supervision.
• Show a capacity to form independent conclusions based on first principles when confronted with incomplete, ambiguous, or rapidly shifting information landscapes.
• Possess strong communication skills necessary to articulate the nuanced value of clinical AI to skeptical medical professionals and institutional stakeholders.
• Maintain intellectual rigor and methodological discipline in all analytical work, with a commitment to transparency and reproducibility.
• Have a history of working within or leading complex, multi stakeholder environments where scientific judgment directly influences strategic outcomes.
• Be comfortable operating at the intersection of data science and clinical medicine, leveraging deep domain expertise to challenge assumptions and refine questions.

Skills & tools

• Demonstrate proficiency in modern AI stacks and tools to accelerate research workflows, including but not limited to data manipulation, modeling frameworks, and experiment tracking.
• Show expertise in clinical research methodology and translational oncology, with familiarity across study designs, endpoints, and evidentiary standards.
• Experience with data integration from heterogeneous sources, including electronic health records, imaging, and molecular datasets, is strongly indicative of capability.
• Familiarity with regulatory landscapes governing clinical AI and oncology product development is considered a significant professional advantage.

Practical notes

• This role is based on-site at our New York headquarters, requiring consistent physical presence to facilitate collaboration and alignment.
• We operate with a flat organizational structure where leadership is earned through demonstrable initiative and measurable output rather than formal hierarchy.