Our client's 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 nontechnical users across the PA experience

Team Size: 15
Top 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.
What skills/attributes are required (please be detailed as to number of years of experience for each skill)?
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)

Skills:
Advanced SQL and Python for analytics, ETL, and automation
Data modeling, warehousing, and pipeline orchestration (cloudnative 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.

Interview: Video, three rounds of interviews