Data Scientist II

Overview

We’re seeking a Machine Learning Data Scientist with deep expertise in healthcare claims data to design, build, and deploy advanced analytics and machine learning modeling solutions. In this role, you’ll transform complex administrative and clinical datasets into actionable insights that improve cost efficiency, care quality, and operational performance across the healthcare ecosystem.

You’ll collaborate with data engineers, clinicians, and product teams to develop predictive models, optimize workflows, and support strategic decision‑making. This position is ideal for someone who thrives at the intersection of data science, healthcare operations, and modern machine learning.

• Machine Learning & Advanced Analytics:
  Develop, train, and deploy ML models for use cases such as:
• Claims cost prediction and utilization forecasting
• Fraud, waste, and abuse detection
• Risk adjustment and member stratification
• Provider performance and network optimization
• Apply modern ML techniques including gradient boosting, deep learning, NLP, and probabilistic modeling
• Capable of applying advanced predictive analytics to correlate disparate datasets and events and derive business value
• Build scalable pipelines for feature engineering, model training, validation, and monitoring
• Analyze and interpret medical, pharmacy, and dental claims (CPT/HCPCS, ICD‑10, DRG, NDC)
• Translate domain knowledge into meaningful features and model strategies

• Partner with clinicians, product managers, and business stakeholders to define problems and measure outcomes
• Communicate complex analytical findings in clear, actionable terms

• Strong proficiency in Python and ML libraries (scikit‑learn, XGBoost, Tensor Flow/PyTorch)
• Hands‑on experience with healthcare claims datasets and coding systems
• Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques
• Strong knowledge and expertise working with SQL
• Ability to translate business needs into analytical solutions
• Must have demonstrated the ability to solve complex problems with minimal direction

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