Arbital Health

Actuary

• Review code, methodology, and analysis used for financial reporting in the healthcare industry
• Contribute to features of the Arbital Platform, especially financial reporting features
• Manage data requirements, evaluate appropriateness of data, and problem solve any issues with internal data and customers’ Claims, Eligibility, and Prescription data
• Collaborate with external stakeholders as needed
• Make recommendations based on analysis and communicate results and solutions to meet client needs in a timely manner
• Increase efficiency through process standardization
• Model financial outcomes, evaluate drivers of healthcare trends, and identify risks
• Leverage your strong backend development skills in Python and with platforms like Databricks and AWS while working on the processing of high volumes of complex healthcare data
• Work closely with Product and Design teams to create a user experience that intuitively conveys complex healthcare and financial information
• Collaborate with an actuarial science team that primarily works in SAS, R, and Excel
• Contribute to the design, implementation, and overall development of our products
• Drive innovation and deliver valuable features for our customers

Actuarial Software Engineer

Data Scientist

• Arbital Health centralizes, measures, and adjudicates value-based care contracts at scale
• Design, train, and test LLMs, RAG frameworks, and ML models for high-value use cases
• Provide cross-functional support to design, validate, and scale AI-driven capabilities on a mission-critical platform
• Analyze large healthcare datasets (claims, revenue, eligibility) to support client engagements and product strategy
• Maintain and enhance feature variable library and grouper logic
• Ensure secure and compliant handling of regulated data (e.g., PHI, HIPAA, financial data)
• Provide clear, actionable bug reports and support QA efforts
• Maintain well-documented, reproducible codebases
• Provide guidance for ethical use of GenAI and machine learning models
• Create and own update processes for model refreshes
• Maintain cleaned and enriched benchmark and de-identified datasets for various applications