We are seeking a Healthcare Claims Python Programmer to join our analytics team and play a key role in transforming healthcare claims data into actionable insights. This position combines technical expertise in Python programming and data analysis with a strong understanding of healthcare claims workflows, reimbursement models, and payer-provider data structures.

The ideal candidate will be passionate about using data to improve operational efficiency, detect anomalies, and support business decisions across claims processing, adjudication, and financial analysis.

Key Responsibilities: 

• Extract, transform, and analyze large healthcare claims datasets from multiple sources (EDI 837, 835, flat files, databases, APIs, etc.).
• Develop and maintain Python-based data pipelines, automation scripts, and analytics workflows to process claims efficiently and accurately.
• Design and implement data quality checks to identify errors, duplicates, and inconsistencies in claims data.
• Collaborate with business stakeholders, actuaries, and data engineers to define analytical requirements and deliver insights.
• Create dashboards, reports, and visualizations using tools such as Tableau, Power BI, or Plotly to monitor claims performance, denial trends, and cost drivers.
• Apply statistical methods and predictive modeling to identify fraud, waste, and abuse or forecast claims volumes and payments.
• Ensure compliance with HIPAA and other healthcare data privacy and security standards.
• Participate in process improvement initiatives to optimize claims workflows and data accuracy.

Key Requirements:

• Bachelor’s degree in Data Science, Computer Science, Statistics, Health Informatics, or related field (or equivalent work experience).
• 2+ years of experience working with healthcare claims data (payer, provider, or clearinghouse).
• Proficiency in Python for data analysis (Pandas, NumPy, PySpark, etc.).
• Strong understanding of healthcare claims formats (EDI 837/835, CPT, ICD-10, HCPCS, DRG).
• Experience with SQL and relational databases (Snowflake, PostgreSQL, SQL Server, etc.).
• Ability to interpret complex healthcare and financial data and communicate findings clearly.