Associate Director, R&d Neuroscience Data, Data Science & AI - Ophthalmology

What you'd actually do

1. Collaborate in the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images), to uncover disease mechanisms and identify novel biomarkers.
2. Collaborate in the development and validation of novel digital endpoints. Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements.
3. Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification. Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making.
4. Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases.
5. Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies. Support trial optimization and regulatory submissions by incorporating insights from large-scale clinical datasets, electronic health records (EHRs), and other real-world data sources.

Skills

What the JD emphasized

• advanced AI/ML methods
• computer vision
• ophthalmic imaging data
• digital biomarkers
• patient stratification
• generative AI
• multimodal datasets
• real-world evidence
• clinical development is required
• model validation
• regulatory considerations

Other signals

• AI/ML methods
• computer vision
• ophthalmic imaging data
• digital biomarkers
• patient stratification
• generative AI
• multimodal datasets
• real-world evidence