Data Science Analyst II

Data Science Analyst II

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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• Demonstrates proficiency in cloud-based analytics environments.
• Adopts emerging cloud tools and MLOps practices.
• Experiments with new ML algorithms and evaluates performance.
• Shares new techniques with peers through code reviews and demos.

• Exhibits a collaborative mindset with strong business acumen.
• Partners with IT, clinicians, and administrators on data projects.
• Resolves conflicts between technical feasibility and operational needs.
• Encourages team knowledge-sharing and joint problem-solving.

Required Qualifications

• Requires a Master's Degree in Data Science, Engineering, Statistics, Computer Science, or related field with at least 3 year(s) of experience in data science, machine learning, or predictive analytics.
• Proficiency in Python or similar language
• Strong SQL and data modeling skills.
• Experience with cloud platforms (Azure, AWS, Google).
• Familiarity with ML frameworks and analytics tools.

Preferred Qualifications

• Doctorate in Data Science, Engineering, Computer Science or related field with at least 5 year(s) of experience in applied ML experience.
• Experience working with healthcare datasets and standards (OMOP, FHIR).
• Experience operationalizing models or using MLOps tools.
• Demonstrated experience in ETL, automation, and at least one cloud environment.
• Experience with clinical informatics data exchange standards and platforms.

Salary Range

$80,000 + depending on qualifications

Working Conditions

• Standard office equipment
• Repetitive use of a keyboard
• May be exposed to such occupational hazards as communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications and disoriented or combative patients, or others.

Required Materials

• Resume/CV
• 3 work references with their contact information; at least one reference should be from a supervisor
• Letter of interest