Director, Precision Medicine Data Science & AI

Band

Level 6

 

Job Description Summary

Location:
#LI-Hybrid

This role sits at the intersection of AI/ML, real-world data, digital health, and commercial pharma strategy within the Strategy, Platforms & Transformation Personalized & Precision Medicine (PPM) team. The Director will architect, develop, deploy, and scale patient identification models, HCP prioritization algorithms, and site-of-care analytics across Novartis's US portfolio.

This is a multidisciplinary role requiring deep technical expertise in machine learning, hands-on implementation experience in healthcare/clinical settings, and the strategic acumen to manage external partnerships with health systems, AI/CDS vendors, real-world data aggregators, and digital health developers. The successful candidate thrives in ambiguity, operates with a high degree of independence, and brings a builder mindset to an evolving precision medicine function.

This position will be located at East Hanover, NJ site and will not have the ability to be located remotely. This position will require 10% travel as defined by the business (domestic and/ or international)

 

Job Description

Key Responsibilities:

• Lead end-to-end development and deployment of patient identification, HCP targeting AI/ML, ensuring clinical validity and commercial impact across therapeutic areas.
• Design clinical decision support (CDS) algorithms to improve patient outcomes via accelerating time to diagnosis, and treatment leveraging real-world data sources including claims, EHR, lab, genomic, patient generated health data, and registry data. Own and advance external partnership strategies with health systems, AI/CDS vendors to integrate these algorithms into clinical workflows leading to measurable impact on clinical outcomes.
• Architect scalable model lifecycle frameworks — from ideation through development, validation, deployment, monitoring, optimization, and sunsetting — ensuring governance, fairness, and reproducibility.
• Navigate regulatory and compliance requirements related to AI/ML in healthcare, including FDA CDS guidance frameworks, HIPAA, and anti-kickback safe harbor considerations.
• Collaborate cross-functionally with Brand/Marketing, Medical Affairs, HEOR, Legal/Compliance, Field Teams, and Patient Support to translate clinical and commercial needs into actionable AI/ML solutions.
• Drive integration of advanced AI technologies including large language models (LLMs), generative AI, deep learning, and foundation models into precision medicine workflows where applicable.
• Develop go-to-market frameworks and field activation resources that enable model-adjacent materials for commercial teams to act on model outputs.
• Champion a culture of experimentation, continuous improvement, and scientific rigor — contributing to peer-reviewed publications, conference presentations, and open-source model externalization where appropriate.
• Operate with a high level of autonomy and self-direction, proactively identifying opportunities, managing ambiguity, and driving initiatives forward without close supervision.
• Serve as a thought leader within Strategy, Platforms & Transformation and across the Novartis US organization on the convergence of AI, real-world data, and precision medicine in commercial pharma.

Essential Requirements:

• Master's degree required; PhD or doctoral degree preferred. Relevant fields: Data Science, Computer Science, Bioinformatics, Health Informatics, Computational Biology, Statistics, Epidemiology, or related quantitative/health sciences discipline. Clinical or health-focused education or research background is a plus.
• Minimum 10+ years of experience in data science, AI/ML, or digital health, with at least 5 years in pharmaceutical, medical device, health-tech, or digital health settings.
• Demonstrated hands-on expertise building and deploying machine learning models (classification, NLP, deep learning, recommendation systems) using Python, Spark, TensorFlow, PyTorch, or equivalent.
• Experience designing and executing model validation frameworks — ensuring accuracy, reliability, clinical validity, and reproducibility of AI/ML algorithms prior to clinical or commercial deployment
• Proven experience with cloud-based ML infrastructure (AWS, Azure, GCP) for scalable model deployment.
• Deep working knowledge of healthcare data: EHR (structured and unstructured), medical/pharmacy claims, lab results, genomic data, and patient registries.
• Working knowledge of clinical terminologies and coding systems (e.g., ICD-10, SNOMED CT, LOINC, RxNorm, CPT) used in model feature engineering and clinical data interpretation
• Proven ability to communicate complex AI/ML concepts and strategies to