Head of AI and Research Data Services, Oncology

Role Overview

• Define an AI roadmap for R&D aligned to Oncology and Pain portfolio priorities (e.g., precision oncology, novel modalities, pain mechanisms, endpoints, patient subtypes).
• Identify, prioritize, and deliver AI initiatives with clear value cases (time-to-decision, probability of technical success, cycle-time reduction, cost efficiency, trial success metrics).
• Establish a use-case portfolio spanning discovery → clinical → real-world evidence, ensuring a balanced mix of quick wins and strategic platforms.
• Lead (and when needed, personally architect) AI/ML solutions such as:
• Oncology: multimodal biomarker discovery (omics + imaging + clinical), pathology/radiology ML, response prediction, resistance mechanisms, molecular stratification, site selection and enrollment optimization.
• Pain: phenotyping and subtyping, digital biomarkers, endpoint optimization (PROs, wearable data), mechanistic modeling, responder prediction, trial enrichment strategies.
• Cross-portfolio: NLP for literature/clinical notes, knowledge graphs for hypothesis generation, causal inference for RWE, safety signal augmentation, protocol optimization and feasibility.
• Partner with Data Engineering/IT to build and evolve the R&D AI stack: data products, feature stores, model registries, reproducibility, and scalable compute.
• Establish MLOps/LLMOps practices (model versioning, monitoring, drift detection, validation, auditability).
• Ensure compliance with relevant regulations and quality expectations (e.g., GxP considerations where applicable, privacy, security, data integrity).
• Create and chair an AI governance framework: model risk classification, validation standards, documentation, bias testing, and human-in-the-loop controls.
• Define policies for vendor tools, generative AI usage, data access, IP protection, and secure development.
• Build and lead a cross-functional team of AI scientists, ML engineers, data scientists, product leads, and scientific translators.
• Serve as a trusted partner to therapeutic area heads, Translational Medicine, Clinical Development, Biometrics, and Regulatory.
• Build strategic collaborations with academia, AI vendors, consortia, and CRO/CDMO partners.
• Represent the company externally (conferences, publications, partner discussions) as an AI leader in Oncology and Pain R&D.

Requirements

• PhD, MD, or equivalent degree in Computer Science, Machine Learning, Statistics, Bioinformatics/Computational Biology, Biomedical Engineering, or a related field with exceptional, demonstrated industry impact and leadership.
• Preferred: formal training or substantial experience in clinical research, oncology biology, neuroscience/pain biology, or translational medicine.
• 10+ years relevant experience applying AI/ML in life sciences, healthcare, biotech/pharma R&D, or adjacent regulated domains; 5+ years leading teams and delivering products.
• Proven track record deploying AI systems beyond prototypes (productionization, adoption, measurable impact).
• Demonstrated experience with multimodal biomedical data (e.g., EHR/RWD, imaging, omics, wearable/digital data, assay data, clinical trial data).
• Strong background partnering with Clinical Development, Biometrics/Stats, Translational Medicine, and/or Discovery teams.
• Experience establishing MLOps practices and governance in environments requiring traceability and documentation.
• Exposure to oncology and/or pain programs (preclinical to clinical). Deep expertise in one with credible breadth across the other is acceptable.
• Experience with trial design optimization, adaptive designs, enrichment strategies, or biomarker-driven development.
• Familiarity with pathology/radiology ML, digital endpoints, or sensor-derived biomarkers.
• Publications, patents, or conference presence in applied AI for biomedicine.
• Experience with knowledge graphs, graph neural networks, or multimodal foundation models in life sciences.
• Prior experience leading through an AI transformation or scaling an AI CoE in R&D.
• Deep knowledge of modern ML (supervised/unsupervised, deep learning, NLP/LLMs, causal inference, time-to-event modeling, multimodal learning).
• Ability to review code/architecture, challenge assumptions, and drive practical engineering outcomes.
• Strong experimental design mindset; understands data leakage, bias, confounding, validation, and generalizability.
• Expertise in cloud-based data solutions, ETL pipeline development, and modern analytics tools (Azure, SAS, R, Python, SQL).
• Strong knowledge of global data standards and regulatory frameworks.
• Hands‑on experience with research‑phase data tools and platforms, including:
• Electronic Lab Notebooks
• Laboratory Information Management Systems
• Bioinformatics workflows
• Omics analysis platforms
• High‑performance computing (HPC) environments
• Version‑controlled research workflows (Git/GitHub/GitLab)
• Data visualization and exploration tools (Spotfire, LiveDesign, etc.)
• Model‑building and experimentation platforms
• Preclinical study data systems

Tech Stack

• Azure
• Cloud
• ETL
• Python
• SQL

Benefits

• The opportunity to shape the future of pharmaceutical research in a global, agile, and entrepreneurial environment.
• Generous health, welfare, and development benefits.
• A leadership role with significant influence on Orion’s sustainability agenda, patient safety, and ethical standards.