Data Scientist (AI, Big Data, SQL, Python) (Need Current Healthcare Exp.)

100% REMOTE Must have Curently Healthcare Exp. Description Job Description: Our audit and governance functions require a centralized data leader who can: • Architect scalable, secure, compliant data pipelines • Translate complex datasets into actionable insights for regulatory and operational decisions • Build intuitive, low?maintenance tools that empower non?technical users across the PA experience Responsibilities • Data Collection & Cleaning - They gather data from various sources and clean it to ensure it's usable—removing errors, filling in missing values, and standardizing formats. • Exploratory Data Analysis (EDA) - They explore the data to understand patterns, trends, and relationships using statistical techniques and visualizations. • Model Building - They build predictive models using machine learning algorithms to forecast outcomes or classify data. • Interpretation & Communication - They translate complex results into actionable insights and communicate them to stakeholders through reports, dashboards, or presentations. • Deployment & Monitoring - In some cases, they help deploy models into production systems and monitor their performance over time. Ideal Background • Healthcare specific background would be helpful. • But candidate must be experienced in elements of statistics, computer science, and domain expertise to help organizations make data-driven decisions. • As well as, build and maintain artificial intelligence (AI) driven platforms/solutions. Required • Programming: Python, R, SQL • Statistics & Mathematics • Machine Learning & AI • Data Visualization: Tools like Tableau, Power BI, or libraries like Matplotlib and Seaborn • Big Data Tools: Spark, Hadoop (for large-scale data) Preferred • Advanced SQL and Python for analytics, ETL, and automation • Data modeling, warehousing, and pipeline orchestration (cloud?native stack) • Dashboarding (Power BI; Streamlit or similar) and reproducible analytics (versioning, CI/CD preferred) • Healthcare data familiarity (claims, PA & appeals, pharmacy) and regulatory contexts (CMS, NCQA, URAC, ERISA, state rules) • Data security, privacy, and compliance best practices.