Healthcare analyst,Pharmacy Technician,Epic
Resume:
Suraparaju Asritha
Missouri Mob - 314-***-**** Email- ***************@*****.***
Professional Summary
Health Data Science graduate student with a pharmacy background and experience working with clinical and population health datasets in healthcare research environments. Skilled in analyzing electronic health records (EHR), public health survey data, and epidemiological datasets to support clinical research and healthcare analytics. Experienced in applying machine learning techniques and statistical methods for disease prediction, healthcare outcome analysis, and population health studies. Proficient in Python, R, and SQL for data cleaning, statistical modeling, predictive analytics, and healthcare data visualization. Familiar with healthcare data standards, clinical research workflows, and analytical approaches used in healthcare systems and public health organizations.
Education
•Master of science in Health Data Science, Saint Louis University, MO Jan 2024 – May 2026
•Bachelor of Pharmacy, Narayanamma College, India Sep 2019 – June 2023
Professional Experience
Healthcare Data Analyst Oct 2022 - Sep 2023
AIG Hospital - Nellore, India
•Analyzed large clinical datasets containing 50,000+ patient records to identify patterns in patient outcomes and hospital operations.
•Performed data cleaning, preprocessing, and exploratory data analysis using Python, SQL, and Excel.
•Developed dashboards and visual reports to support clinical and operational decision making.
•Extracted structured healthcare data using SQL queries for analytical workflows and reporting.
•Maintained structured clinical documentation aligned with healthcare data compliance standards.
Clinical Junior Pharmacist Jun 2021 - Jun 2022
Sai Ganesh Hospitals - India
•Processed 80–100 prescriptions daily across OPD and IPD departments while maintaining medication accuracy.
•Verified medication dosages and monitored prescriptions to prevent adverse drug interactions.
•Maintained patient medication records and pharmacy documentation systems.
•Conducted pharmacy inventory audits and supported medication compliance checks.
•Collaborated with physicians and nursing staff to support patient treatment workflows.
Industrial Trainee - Quality & Data Documentation Aug 2022 - Sep 2022
Relez Pharma Pvt. Ltd. - Bangalore, India
•Assisted in pharmaceutical regulatory documentation and quality assurance processes.
•Maintained batch manufacturing records in accordance with Good Manufacturing Practices (GMP).
•Supported internal quality audits and regulatory submission preparation.
•Observed pharmacovigilance workflows including drug safety monitoring and adverse event reporting.
Projects
Capstone: Geographic Patterns of Type 2 Diabetes Among U.S. Adults Using BRFSS Data (2022 - 2024)
•Analyzed Behavioral Risk Factor Surveillance System (BRFSS) datasets with 400,000+ survey responses annually.
•Conducted data cleaning, variable harmonization, and multi-year dataset integration using R.
•Applied survey-weighted statistical analysis using the R survey package to account for complex sampling design.
•Estimated state-level prevalence of diabetes, obesity, and comorbidity.
•Built logistic regression models examining associations between diabetes and demographic variables.
•Developed state-level maps and visualizations using ggplot2 to identify geographic disparities.
Sepsis Risk Prediction Using Clinical Patient Data
•Built predictive machine learning models using a synthetic clinical dataset containing 10,000 patient health records and 14 clinical variables.
•Performed exploratory data analysis and feature selection using patient demographics, vital signs, and laboratory measurements.
•Implemented Logistic Regression, KNN, SVM, and Random Forest models for sepsis prediction.
•Achieved model accuracy greater than 90% and evaluated performance using confusion matrices and classification metrics.
•Identified glucose as the most significant predictor of sepsis risk using feature importance analysis.
Facial Emotion Recognition Using Deep Learning (VGG19)
•Developed a deep learning model using VGG19 architecture for emotion classification.
•Trained the model on a facial emotion dataset containing approximately 35,000 images.
•Performed preprocessing including grayscale conversion, resizing (48x48), and data augmentation.
•Implemented real-time emotion detection using OpenCV and webcam integration.
•Achieved 58.37% classification accuracy on the test dataset.
Exploratory Data Analysis of U.S. Mortality Trends
•Analyzed CDC mortality data (1999 - 2017) to study trends in leading causes of death in the United States.
•Conducted data cleaning and exploratory data analysis using R.
•Developed visualizations to identify long-term mortality patterns across disease categories.
Veterans Health Administration Reform Policy Analysis
•Conducted policy evaluation of the VA MISSION Act and PACT Act to assess impact on veteran healthcare access.
•Analyzed policy outcomes including 1.5M+ approved claims and expanded eligibility for veteran healthcare services.
•Evaluated implementation challenges including administrative processing delays and community care data gaps.
•Developed policy insights on healthcare access improvements and telehealth expansion for veteran populations.
Skills
Programming: Python, R, SQL
Data Analysis: Exploratory Data Analysis (EDA), Data Cleaning, Feature Engineering, Statistical Modeling, Survey Data Analysis
Machine Learning: Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors, Deep Learning (CNN)
Libraries & Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow, Matplotlib, Seaborn.
Tools & Platforms: GitHub, Databricks, Snowflake, Microsoft Excel
Healthcare Analytics: Electronic Health Records (EHR), Clinical Data Analysis, Public Health Data, Healthcare Policy Analysis
Certifications
•Good Clinical Practice (GCP) – NIDA Clinical Trials Network
•CITI Program – Biomedical Research Compliance
•Acute Ischemic Stroke Evaluation and Management – Cleveland Clinic.
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