Salud Revenue Partners (Salud) is looking to establish and lead an AI division to focus on high-impact solutions in predictive analytics, natural language processing (NLP), and recommendation systems to improve revenue cycle performance for healthcare providers.
Requirements
- Expert in machine learning algorithms and statistical modeling techniques.
- Proficient in programming with Python (pandas, scikit-learn, TensorFlow/PyTorch) and SQL; familiarity with R or other languages is a bonus.
- Experience with data visualization tools and libraries to communicate data insights.
- Comfortable with big data technologies (e.g., Spark, Hadoop) and cloud ML services (AWS, GCP, or Azure ML stack) for scaling models.
- Experience with Azure ML specifically is a plus.
- Solid understanding of software engineering practices (version control, code reviews) and MLOps concepts for model deployment and lifecycle management.
- Demonstrated success in at least two of the following: predictive analytics, NLP, or recommendation systems.
Responsibilities
- Act as the technical and strategic leader for the AI function.
- Lead the development of machine learning models that use structured data to perform classification, regression, forecasting, and risk prediction.
- Design and implement NLP solutions including document classification, entity recognition, text summarization, and semantic analysis.
- Architect recommendation systems for personalization and next-best-action applications.
- Collaborate closely with data engineering and software development teams to source, prepare, and manage structured and unstructured data.
- Ensure machine learning models are integrated securely and scalable into production environments through APIs or cloud platforms.
- Provide technical guidance and best practices in data science (coding standards, experimentation, documentation) to both the AI team and the wider organization's analysts who might benefit from your expertise.
Other
- Partner with executive and operational stakeholders to identify use cases, define success criteria, and ensure that AI initiatives are aligned with organizational goals.
- As the division grows, participate in hiring additional data scientists or ML engineers.
- Mentor team members and foster an environment of learning and innovation.
- Stay current with academic and industry developments in AI.
- Excellent leadership and project management skills ' capable of setting clear goals, meeting deadlines, and coordinating cross-functional efforts.
