Job Description

• Create Machine Learning models in support of a behavioral health data analytics team 
• Leverage Python/R for data analysis
• Apply clustering, regression, NLP, predictive modeling to create informative Machine Learning models 
• Collaborate cross-functionally with public health teams, clinicians, and leadership to translate data into actionable strategies for quality improvement
• Conduct audits, data validation, and performance analyses to ensure data quality, regulatory compliance, and accurate reporting
• Contribute to AI/ML-enabled digital health solutions, including mental health risk classifiers and wellness monitoring tools
• Possess exceptional business acumen and outstanding communication to present findings and visualizations to executives and technical/ non-technical stakeholders

• Bachelor’s degree required (data science, statistics, public health, healthcare analytics, or related field).

• 1-3+ years of experience with statistical analysis and predictive analytics using Python or R
• Experience with Machine Learning model development for healthcare quality and population health analytics
• Experience with predictive modeling techniques including classification, regression, risk scoring, and stratification
• Experience with ML libraries/frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar
• Experience working with large, structured healthcare datasets
• Experience with healthcare data analysis: claims, encounter, EHR, member and provider datasets
• Ability to analyze healthcare quality metrics (HEDIS, CMS, NCQA, preferred)
• Knowledge of population health analytics and member risk segmentation

• Experience with data preprocessing, feature engineering, and model validation, preferred
• Experience with SQL querying and relational database analysis, preferred
• Experience with data visualization and reporting using Power BI, Tableau, or similar BI tools, highly preferred 
• Knowledge of healthcare interoperability and coding standards (ICD-10, CPT, HCPCS, SNOMED, LOINC)
• Understanding of healthcare utilization, care management, and quality improvement initiatives
• Familiarity with CMS, NCQA, and HEDIS reporting methodologies
• Model performance evaluation using precision/recall, ROC-AUC, confusion matrices, and cross-validation
• Experience developing dashboards, KPIs, and analytical reporting for healthcare stakeholders
• Exposure to cloud analytics (AWS, Azure, or GCP) preferred   

• Hanover, MD- HYBRID (1 day a week in office, required)