Healthcare Statistical Data Scientist (ML)

TL;DR

Healthcare Statistical Data Scientist (ML): Designing and deploying ML modeling solutions for healthcare claims data with an accent on cost efficiency, care quality, and operational performance. Focus on building predictive models for fraud detection, risk adjustment, and utilization forecasting.

Company

hirify.global combats fraud, waste, and abuse in healthcare through proprietary data analysis and model development.

What you will do

• Develop, train, and deploy ML models for claims cost prediction, utilization forecasting, and fraud detection.
• Apply advanced techniques including gradient boosting, deep learning, NLP, and probabilistic modeling.
• Build scalable pipelines for feature engineering, model training, validation, and monitoring.
• Analyze and interpret medical, pharmacy, and dental claims using CPT/HCPCS, ICD-10, DRG, and NDC.
• Collaborate with clinicians, product managers, and stakeholders to translate business needs into analytical solutions.

Requirements

• Strong proficiency in Python and ML libraries (scikit-learn, XGBoost, TensorFlow/PyTorch).
• Hands-on experience with healthcare claims datasets and coding systems.
• Strong knowledge and expertise working with SQL.
• Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques.
• Demonstrated ability to solve complex problems with minimal direction.

Nice to have

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