Role: Senior Health Informatics / Data Scientist Location: Remote (U.S.) Duration: 6+ Month Contract Working Time Zone: PST (Pacific Time Zone)
Position Overview
We are seeking a Senior Health Informatics / Data Scientist with deep technical expertise in statistical modeling, machine learning, and healthcare data analytics. This is a hands-on role focused on developing data science models using Python to support risk stratification and risk tier migration initiatives within the U.S. healthcare ecosystem.
The ideal candidate will have experience working with healthcare claims and population health datasets, building predictive models that support patient risk identification and care management strategies. The role requires strong familiarity with Medicare, Medicaid, and other U.S. healthcare reimbursement structures and coding frameworks.
This individual will collaborate with analytics, clinical, and population health teams to develop models that help identify high-risk populations and support value-based care initiatives.
Key Responsibilities
Develop and implement statistical and machine learning models for healthcare risk stratification and population health analytics.
Build predictive models to support risk tier migration and risk adjustment strategies.
Use Python and modern data science libraries (pandas, NumPy, scikit-learn, etc.) to design, test, and deploy analytical models.
Analyze large-scale healthcare datasets including claims, clinical, and demographic data.
Work with healthcare stakeholders to translate analytical findings into actionable insights for care management and population health initiatives.
Identify high-risk patient cohorts and support targeted intervention strategies.
Ensure models and analytics align with Medicare, Medicaid, and other reimbursement frameworks.
Document methodologies and present insights to both technical and non-technical stakeholders.
Required Qualifications
Advanced degree in Data Science, Statistics, Biostatistics, Computer Science, Health Informatics, or related quantitative field.
Strong experience in statistical modeling, machine learning, and predictive analytics.
Hands-on Python programming experience for data science and model development.
Experience working with healthcare claims data, population health data, or clinical datasets.
Demonstrated experience with risk stratification, risk adjustment, or population health modeling.
Strong analytical and problem-solving skills.
Preferred Qualifications
Experience with risk tier migration analytics within healthcare organizations or health plans.
Familiarity with Medicare and Medicaid reimbursement structures.
Knowledge of healthcare coding standards such as ICD, CPT, and HCPCS codes.
Experience working with health plans, healthcare analytics firms, provider organizations, or consulting firms.
Role: Senior Health Informatics / Data Scientist Location: Remote (U.S.) Duration: 6+ Month Contract Working Time Zone: PST (Pacific Time Zone)
Position Overview
We are seeking a Senior Health Informatics / Data Scientist with deep technical expertise in statistical modeling, machine learning, and healthcare data analytics. This is a hands-on role focused on developing data science models using Python to support risk stratification and risk tier migration initiatives within the U.S. healthcare ecosystem.
The ideal candidate will have experience working with healthcare claims and population health datasets, building predictive models that support patient risk identification and care management strategies. The role requires strong familiarity with Medicare, Medicaid, and other U.S. healthcare reimbursement structures and coding frameworks.
This individual will collaborate with analytics, clinical, and population health teams to develop models that help identify high-risk populations and support value-based care initiatives.
Key Responsibilities
Develop and implement statistical and machine learning models for healthcare risk stratification and population health analytics.
Build predictive models to support risk tier migration and risk adjustment strategies.
Use Python and modern data science libraries (pandas, NumPy, scikit-learn, etc.) to design, test, and deploy analytical models.
Analyze large-scale healthcare datasets including claims, clinical, and demographic data.
Work with healthcare stakeholders to translate analytical findings into actionable insights for care management and population health initiatives.
Identify high-risk patient cohorts and support targeted intervention strategies.
Ensure models and analytics align with Medicare, Medicaid, and other reimbursement frameworks.
Document methodologies and present insights to both technical and non-technical stakeholders.
Required Qualifications
Advanced degree in Data Science, Statistics, Biostatistics, Computer Science, Health Informatics, or related quantitative field.
Strong experience in statistical modeling, machine learning, and predictive analytics.
Hands-on Python programming experience for data science and model development.
Experience working with healthcare claims data, population health data, or clinical datasets.
Demonstrated experience with risk stratification, risk adjustment, or population health modeling.
Strong analytical and problem-solving skills.
Preferred Qualifications
Experience with risk tier migration analytics within healthcare organizations or health plans.
Familiarity with Medicare and Medicaid reimbursement structures.
Knowledge of healthcare coding standards such as ICD, CPT, and HCPCS codes.
Experience working with health plans, healthcare analytics firms, provider organizations, or consulting firms.
