Healthcare Statistical Data Scientist (ML)

TL;DR

Healthcare Statistical Data Scientist (ML): Building and deploying advanced machine learning models for healthcare claims analysis with an accent on cost prediction, fraud detection, and risk stratification. Focus on transforming complex clinical datasets into actionable insights and developing scalable pipelines for predictive modeling.

Location: Remote

Company

hirify.global is a healthcare-focused company dedicated to reducing costs and combating fraud, waste, and abuse through proprietary data analysis and machine learning.

What you will do

• Develop and deploy ML models for claims cost prediction, fraud detection, and provider performance optimization.
• Analyze and interpret complex medical, pharmacy, and dental claims data using industry-standard coding systems.
• Build scalable pipelines for feature engineering, model training, validation, and monitoring.
• Collaborate with clinicians, product managers, and business stakeholders to define analytical problems and measure outcomes.
• Translate complex analytical findings into clear, actionable business insights.

Requirements

• Strong proficiency in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Hands-on experience working with healthcare claims datasets and coding systems like CPT, HCPCS, ICD-10, and DRG.
• Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques.
• Expertise in SQL for data extraction and manipulation.
• Proven ability to solve complex problems with minimal direction.

Nice to have

• Experience with NLP for clinical notes or unstructured healthcare data.
• Familiarity with actuarial concepts, risk scoring, or value-based care models.
• Experience deploying models into production using MLOps and CI/CD practices.
• Background in health economics, epidemiology, or biostatistics.
• Prior work with FHIR, HL7, or healthcare interoperability standards.