Responsibilities:

• Develop and deploy predictive models using advanced AI and machine learning methodologies.
• Continuously integrate diverse real-time data streams and ensure model adaptability and accuracy.
• Conduct rigorous validation, optimization, and refinement of predictive models using statistical and data-driven techniques.
• Work collaboratively with domain experts to translate analytical insights into actionable solutions.
• Document algorithmic processes, methodologies, model validations, and optimizations comprehensively.

Requirements:

• PhD in Computer Science, Data Science, Statistics, or related field.
• Knowledge of Causal Bayesian Networks
• Advanced degrees or AI/ML certifications are a plus.
• Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch).
• Hands-on experience with MLOps for model deployment and monitoring.
• Ability to handle large datasets and ensure data quality.
• Expertise in building and optimizing machine learning models.
• Strong analytical and troubleshooting skills.
• Prior experience in healthcare or medical data analytics.
• Familiarity with clinical data standards, interoperability, and healthcare informatics.
• Experience in production-level model deployment and real-time data integration.
• Clear communication of technical concepts to diverse audiences.
• Team player with strong collaboration and mentoring abilities.
• Stay updated on advancements in AI, machine learning, and tools.