Data Science Analyst II - Healthcare AI & Analytics

Job Posting

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Position Open To:

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Weekly Scheduled

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FLSA Status:

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Earliest

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Position Duration:

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Job Details:

Purpose

The Data Science Analyst II is responsible for developing and deploying advanced analytics, machine learning models, and data pipelines to support enterprise and clinical decision making. This role partners with clinical and administrative leaders to translate complex business problems into scalable data science and AI solutions, contributes to predictive modeling and automation initiatives, and mentors junior analysts. Working closely with data architects, engineers, informaticians, and clinicians, the Data Science Analyst II helps design and implement innovative analytic solutions-often at the point of care-that drive systemwide performance improvement.

Responsibilities

Advanced Data Science and Modeling

• Designs and develops predictive models using advanced ML methods.
• Performs feature engineering, model evaluation, and hyperparameter tuning.
• Builds and tests prototypes for deployment in clinical or operational workflows.
• Conducts scenario modeling, pattern detection, and trend forecasting.
• Monitors models for performance and drift
• Synthesizes findings into meaningful insights and recommendations.

Data Integration and Pipeline Development

• Integrates structured and unstructured data from multiple enterprise systems.
• Builds and maintains automated pipelines, ETL processes, and reproducible scripts.
• Uses code repositories and CI/CD methods for model and analytics deployment.
• Ensures data accuracy through validation and rigorous quality checks.
• Partners with IT and data engineering to optimize architecture.

Data Visualization and Decision Support

• Develops advanced dashboards and interactive tools.
• Automates recurring modeling outputs and analytics workflows.
• Ensures consistency of model-driven KPIs across departments.
• Creates visualizations that simplify complex findings.

Stakeholder Engagement and Consultation

• Serves as a data science consultant to clinical and operational leaders.
• Translates ambiguous questions into structured analytical methods.
• Leads meetings to gather requirements and present insights.
• Guides teams on the interpretation of AI/ML outputs.

Mentorship and Project Leadership

• Mentors junior analysts and reviews modeling work.
• Leads small-to-medium-sized data science projects.
• Defines milestones, tracks progress, and communicates with stakeholders.
• Contributes to the development of data science best practices.

Marginal or Periodic Functions:

• Evaluates emerging AI/ML tools and cloud technologies to guide enterprise adoption and architecture decisions.
• Ensures data science workflows comply with security, HIPAA, and institutional standards through periodic reviews.
• Audits and remediates model performance after drift, regulatory changes, or major data-source updates to maintain safe clinical integration..
• Adheres to internal controls and reporting structure.
• Performs related duties as required.

Knowledge/Skills/Abilities:

Functional/Technical Skills

• Demonstrates a strong understanding of advanced statistical and ML techniques.
• Applies advanced ML/statistical methods to build predictive models.
• Maintains proficiency in Python, SQL, and ML frameworks.
• Ensures data integrity across complex pipelines and ETL processes.

Priority Setting

• Possesses the ability to manage complex analytical workflows and multiple priorities.
• Balances multiple analytics projects and deadlines effectively.
• Allocates resources to high-impact modeling initiatives.
• Adjusts priorities when urgent clinical needs arise.

Communicating Effectively

• Communicates effectively and simplifies technical concepts.
• Translates technical findings into actionable insights for leaders.
• Creates visualizations that make complex data understandable.
• Adapts communication style for technical and non-technical audiences.

Technical Learning

• Demonstrates proficiency in cloud-based analytics environments.
• Adopts emerging cloud tools and MLOps practices.
• Experiments with new ML algorithms and…