Karthik Daddanala
Professional Summary
Senior Data Analyst with 3+ years of experience delivering analytics and predictive modeling for healthcare and pharmaceutical claims. Expert in SQL and Python with additional proficiency in R to extract, clean, and transform large PBM and medical claims datasets for downstream modeling. Built time-series forecasts, regression and classification models, clustering and anomaly-detection pipelines, and reusable ETL workflows to support utilization management and drug cost optimization. Experienced in automating reporting and producing client-ready dashboards using Python and Excel while ensuring HIPAA-compliant data governance. Collaborated with clinicians, consultants, and product teams to translate business questions into analytical solutions and supported model deployment and operationalization for measurable clinical and financial impact.
Technical Skills
Work Experience
- Analyzed pharmacy and medical claims with complex SQL queries to identify cost drivers and utilization patterns across PBM datasets.
- Designed and trained time-series forecasting models in Python using pandas and scikit-learn to predict short-term pharmacy spend and inform client planning.
- Implemented regression and classification models in R to segment high-cost cohorts and prioritize utilization management interventions.
- Built reusable Python pipelines to clean, validate, and transform raw claims data, improving data consistency for downstream models and dashboards.
- Automated report generation and delivery using Python scripts and advanced Excel techniques to streamline client reporting workflows.
- Ensured HIPAA-compliant data handling and enforced data governance controls across analytical pipelines and model deployment processes.
- Performed large-scale SQL extraction and complex joins across relational claims databases to prepare datasets containing millions of pharmacy and medical claims.
- Developed clustering and anomaly-detection workflows in Python and R to surface irregular PBM patterns and potential fraudulent claims.
- Developed classification models in Python using scikit-learn to segment high-risk members for targeted utilization management programs.
- Authored reusable ETL scripts and validation routines in Python to clean and normalize raw PBM data, improving model input stability and reproducibility.
- Documented analytical methods, data definitions, and model logic to support transparency, data governance, and HIPAA-aligned compliance.
- Backed dashboard development and reporting automation using Excel and Python scripts to reduce manual report assembly and accelerate client delivery.
Projects
- Built end-to-end predictive pipelines in Python and SQL to analyze pharmacy and medical claims across 500K+ member records to identify high-cost drug opportunities.
- Engineered features and trained regression and time-series models to forecast quarterly pharmacy spend and prioritize cost-management actions.
- Designed reusable ETL workflows and automated dashboards to reduce manual reporting time by 40% while preserving HIPAA-compliant data controls.
- Developed an unsupervised clustering and anomaly-detection solution using Python and R to surface irregular PBM patterns and suspicious claims.
- Applied classification and statistical modeling to segment member populations by utilization risk and support targeted clinical interventions.
- Cleaned and validated datasets containing millions of claims and delivered findings through automated reports and stakeholder dashboards.
Education
Certifications
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