NewYork-Presbyterian (NYP) is seeking a Director of Data Science to lead their data science function, overseeing the development of cutting-edge models, algorithms, and data products to drive decision-making and innovation across its affiliated institutions. The role aims to translate data insights into actionable strategies for clinical, operational, and business improvements, ensuring regulatory compliance and enhancing patient outcomes.
Requirements
- Proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow), R, and SQL.
- Experience with big data tools (Spark, Snowflake) and cloud platforms (AWS, GCP, Azure Health Data Services).
- Familiarity with healthcare data tools and platforms such as OMOP/OHDSI, Epic
- Hands-on with NLP tools (e.g., spaCy, transformers, cTAKES, MedSpaCy) for unstructured clinical text.
- Guide the development of machine learning and statistical models trained on complex healthcare datasets, including: EHRs (Electronic Health Records), Patient-reported outcomes, Genomic and imaging data (if applicable)
- Lead efforts in model validation, clinical interpretability, and bias mitigation.
- Collaborate with clinical and compliance teams to ensure models meet standards for fairness, accountability, and transparency.
Responsibilities
- Overseeing the development of cutting-edge models, algorithms, and data products that power decision-making and innovation
- Define the Data Shared Service data science strategy
- Lead teams to build AI/ML models
- Translates data insights into actionable strategies for clinical, operational, and business improvements
- Developing scalable analytics
- Ensuring regulatory compliance (like HIPAA)
- Communicating complex findings to stakeholders to drive innovation and better patient outcomes
Other
- Consults with tri-institutional senior administrative and clinical leaders to define information requirements, data science services, and deliverables to meet the needs of the enterprise.
- Hire, lead, and mentor a multidisciplinary team of data scientists, clinical informaticists, and ML engineers.
- Create training pathways that combine machine learning and clinical domain knowledge.
- Foster a culture of collaboration between technical, clinical, and operational stakeholders.
- Requires on-site presence, four days a week; therefore, should live within a commutable distance. No relocation assistance available.
