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, and build intuitive, low-maintenance tools that empower non-technical users across the PA experience.
Requirements
- 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)
- Advanced SQL and Python for analytics, ETL, and automation
- Data modeling, warehousing, and pipeline orchestration (cloud?native stack)
Responsibilities
- 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
- 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.
Other
- Healthcare specific background would be helpful.
- 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.
- 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)
