Data Scientist

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