Head of AI and Research Data Services, Oncology Therapy Area, Research and Development

Orion Pharma•Boston, MA

3d•Hybrid

About The Position

Orion Pharma is seeking an inspiring and visionary leader to serve as Head of Artificial Intelligence and Research Data Services, guiding our global team through a pivotal phase of digital transformation. The Head of AI and Research Data Services in R&D will define and deliver the AI strategy across Oncology and Pain portfolios, accelerating discovery, translational medicine, and clinical development through high-impact, fit-for-purpose AI/ML solutions. This is a visionary, and proven leadership role: you will set direction, build and mentor a high-performing team, and personally drive the most critical technical and executional workstreams—turning data into decisions and decisions into measurable portfolio outcomes. You will partner deeply with Oncology and Pain R&D leaders, Biometrics/Stats, Clinical Operations, Pharmacovigilance/Safety, Regulatory, Medical Affairs, and IT to deploy robust AI products and platforms that improve target and biomarker discovery, patient stratification, trial design and execution, endpoint measurement, and evidence generation. You will establish modern AI governance and Responsible AI practices aligned with regulated R&D environments and ensure scalable adoption across the organization.

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.
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.
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.
A visionary, hands-on leader who defines a compelling AI strategy tightly linked to portfolio priorities and scientific hypotheses, designs scalable operating models (platform + products + embedded translators), and anticipates where AI can transform R&D decision-making beyond automation to insight.
Prioritizes and turns ambiguity into clear roadmaps, milestones, and measurable outcomes—prototyping fast and industrializing deliberately—while delivering adoption-ready solutions through workflow integration, training, monitoring, and lifecycle management.
Communicates complex AI topics effectively to scientific, clinical, and executive audiences, leads through influence in a matrix, resolves conflict, and aligns stakeholders with credibility across domain and technical teams.
Sets high standards for reproducibility, documentation, privacy, security, and model risk, champions Responsible AI with patient-centered principles, builds and develops high-performing teams and culture, stays scientifically grounded to avoid “AI theater,” and measures success by better portfolio decisions and patients helped.

Nice To Haves

Formal training or substantial experience in clinical research, oncology biology, neuroscience/pain biology, or translational medicine.
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.

Responsibilities

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.
Drive adoption with guardrails—enabling speed while protecting patients, science integrity, and reputation.
Build and lead a cross-functional team of AI scientists, ML engineers, data scientists, product leads, and scientific translators.
Establish a culture of scientific rigor, rapid iteration, accountability, and collaboration.
Coach stakeholders and elevate AI literacy across R&D; create “AI champions” embedded in Oncology and Pain functions.
Serve as a trusted partner to therapeutic area heads, Translational Medicine, Clinical Development, Biometrics, and Regulatory.
Translate R&D needs into technical solutions and ensure adoption through clear workflows and training.
Communicate results effectively to executives and governance bodies; make complex topics accessible and decision-relevant.
Build strategic collaborations with academia, AI vendors, consortia, and CRO/CDMO partners.
Evaluate and integrate external datasets (RWD, imaging, genomics) and technologies while ensuring quality, privacy, and contracts align with strategy.
Represent the company externally (conferences, publications, partner discussions) as an AI leader in Oncology and Pain R&D.