Director, Precision Medicine Data Science & AI
Novartis
22 days ago
On-site
East Hanover, NJ
IT
Key Responsibilities:
- Lead end-to-end development and deployment of patient identification, HCP targeting AI/ML, ensuring clinical validity and commercial impact.
- Design clinical decision support (CDS) algorithms using real-world data (claims, EHR, labs, genomic, patient-generated data, registries); integrate into clinical workflows via external partnerships.
- Architect scalable model lifecycle frameworks (ideation → development, validation, deployment, monitoring, optimization, sunsetting) with governance, fairness, and reproducibility.
- Navigate AI/ML healthcare regulatory/compliance needs (FDA CDS guidance, HIPAA, anti-kickback safe harbor considerations).
- Collaborate cross-functionally (Brand/Marketing, Medical Affairs, HEOR, Legal/Compliance, Field Teams, Patient Support) to translate needs into AI/ML solutions.
- Integrate advanced AI (LLMs, generative AI, deep learning, foundation models) into precision medicine workflows.
- Develop go-to-market and field activation resources enabling teams to act on model outputs.
- Drive experimentation, continuous improvement, and scientific rigor (publications, presentations, open-source externalization as appropriate).
- Serve as a thought leader on AI + real-world data + precision medicine.
Essential Requirements:
- Master’s required (PhD preferred) in relevant quantitative/health fields.
- 10+ years in data science/AI/ML/digital health; 5+ years in pharma/medical device/health-tech/digital health.
- Hands-on ML deployment using Python/Spark and ML frameworks (e.g., TensorFlow, PyTorch).
- Experience building model validation frameworks (accuracy, reliability, clinical validity, reproducibility).
- Cloud ML experience (AWS/Azure/GCP).
- Deep knowledge of healthcare data (EHR, claims, labs, genomics, registries) and clinical coding/terminologies (ICD-10, SNOMED CT, LOINC, RxNorm, CPT).
- Ability to communicate AI/ML to technical and non-technical stakeholders.
- Experience integrating AI/ML into clinical/EHR workflows; manage external partnerships.
- Working knowledge of AI healthcare compliance landscape.
- Experience influencing in matrixed organizations; LLM knowledge (e.g., GPT, BERT, Cohere).
Desirable Requirements:
- External thought leadership (publications, conferences, industry working groups).
- Lead end-to-end development and deployment of patient identification, HCP targeting AI/ML, ensuring clinical validity and commercial impact.
- Design clinical decision support (CDS) algorithms using real-world data (claims, EHR, labs, genomic, patient-generated data, registries); integrate into clinical workflows via external partnerships.
- Architect scalable model lifecycle frameworks (ideation → development, validation, deployment, monitoring, optimization, sunsetting) with governance, fairness, and reproducibility.
- Navigate AI/ML healthcare regulatory/compliance needs (FDA CDS guidance, HIPAA, anti-kickback safe harbor considerations).
- Collaborate cross-functionally (Brand/Marketing, Medical Affairs, HEOR, Legal/Compliance, Field Teams, Patient Support) to translate needs into AI/ML solutions.
- Integrate advanced AI (LLMs, generative AI, deep learning, foundation models) into precision medicine workflows.
- Develop go-to-market and field activation resources enabling teams to act on model outputs.
- Drive experimentation, continuous improvement, and scientific rigor (publications, presentations, open-source externalization as appropriate).
- Serve as a thought leader on AI + real-world data + precision medicine.
Essential Requirements:
- Master’s required (PhD preferred) in relevant quantitative/health fields.
- 10+ years in data science/AI/ML/digital health; 5+ years in pharma/medical device/health-tech/digital health.
- Hands-on ML deployment using Python/Spark and ML frameworks (e.g., TensorFlow, PyTorch).
- Experience building model validation frameworks (accuracy, reliability, clinical validity, reproducibility).
- Cloud ML experience (AWS/Azure/GCP).
- Deep knowledge of healthcare data (EHR, claims, labs, genomics, registries) and clinical coding/terminologies (ICD-10, SNOMED CT, LOINC, RxNorm, CPT).
- Ability to communicate AI/ML to technical and non-technical stakeholders.
- Experience integrating AI/ML into clinical/EHR workflows; manage external partnerships.
- Working knowledge of AI healthcare compliance landscape.
- Experience influencing in matrixed organizations; LLM knowledge (e.g., GPT, BERT, Cohere).
Desirable Requirements:
- External thought leadership (publications, conferences, industry working groups).