Website Expert In Recruitment Solutions
Expert In Recruitment Solutions
Company : Expert In Recruitment Solutions
Data Scientist (AI, Big Data, SQL, Python)| REMOTE
Minneapolis, MN – Remote
Job Description
100% Telecommute
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.