Data Scientist (AI, Big Data, SQL, Python) - REMOTE

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Resource Point LLc, is seeking the following. Apply via Dice today! Job Title: Data Scientist (AI, Big Data, SQL, Python) - REMOTE Location: Minneapolis, MN Duration: 12+ Months 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 Skills: 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 .