Data Scientist II (Healthcare)

Location: Fully remote within the United States; occasional travel to U.S. headquarters may be required for onboarding, team meetings, and company events.

Salary: $137,000–$161,000 per year for a 40-hour work week.

Company

hirify.global works with healthcare claims, authorization, clinical, and operational data to support analytical decision-making.

What you will do

• Gather business requirements and apply trend analysis to identify actionable insights.
• Analyze healthcare claims, authorization, EMR/EHR, pharmacy, and Social Determinants of Health data to identify outliers, patterns, trends, and opportunities.
• Train, fine-tune, and implement LLMs and deep learning models for anomaly detection, classification, and automated analysis.
• Develop data models, algorithms, simulations, and scalable pipelines to support analytical objectives.
• Assess impacts and return on investment related to medical expenses, administrative costs, and clinical outcomes.
• Build Tableau visualizations and dashboards, document methodologies, and present findings to leadership and stakeholders.

Requirements

• Must be able to work from within the United States.
• Master’s degree in Data Science, Statistics, Biostatistics, or a related field; foreign education equivalents are accepted.
• At least 36 months of experience as an analyst or in a related position analyzing datasets.
• Three years of experience analyzing healthcare datasets, performing data quality analysis, and identifying gaps or inconsistencies.
• Experience building AWS Airflow pipelines with Python, PySpark, and AWS S3, and using AWS SageMaker for scalable data analysis.
• Experience with regression analysis, outlier detection, Python, Spark, SQL, Tableau, NLP, LLM development, fine-tuning, algorithms, and simulations.

Culture & Benefits

• Fully remote work from anywhere within the United States.
• Occasional travel to U.S. headquarters for onboarding, team meetings, and company events.
• Opportunity to apply data science and machine learning to healthcare operational and clinical outcomes.