A tech-savvy professional seeking analytical roles.

Priyadarshini Raghavendra

*************.*******@*****.*** Buffalo, NY +1-248-***-**** LinkedIn Portfolio EDUCATION

UNIVERSITY AT BUFFALO - SUNY - January 2023 - May 2024 Master of Science in Engineering Science: Data Science - CGPA 3.26 PROFESSIONAL EXPERIENCE

Full-Stack Software Engineer Oracle Health July 2019 - November 2022

• Led cross-functional teams to align technical solutions with business objectives. Defined user stories, and Key Performance Indicators (KPIs) to guide feature development and measure impact. Facilitated efficient collaboration among development, QA and architect teams through retrospective sessions, to deliver critical product enhancements on time.

• Successfully managed the software development lifecycle (SDLC) using Agile methodology, maintaining a low defect count and achieving a burn rate of 10 story points per sprint. Led sprint planning and product backlog management, and crafted roadmaps and functional flows, working closely with designers, other developers, and QA specialists.

• Collaborated with key stakeholders to gather, analyze, and prioritize business requirements and user needs, designing detailed business specifications using Unified Modeling Language, including use cases, activity diagrams, and flowcharts.

• Performed root cause analysis (RCA) to troubleshoot user-reported issues within 1-3 days, achieving a 70% decrease in backlog and timely delivery of high-risk solutions, improving company/client relationship.

• Conceptualized, developed and deployed a C# .NET end-to-end user interface employing system design and SQL for stored procedures for tracking baby bottle administration, improving nurse usability and error resolution. Integrated CI/CD through Jenkins, gaining widespread client adoption leading to $1.89 million in revenue.

• Enhanced data processing capabilities for large datasets using Flask, Kafka and Python, increasing throughput by 30%. Created intuitive data visualizations with Tableau, improving decision-making processes. Software Intern Oracle Health January 2019 - June 2019

• Developed a C# tool to streamline prerequisite software installation process for Cerner’s EHR platform ‘Millennium’.

• Enhanced productivity, benefiting 90% of Cerner employees by reducing setup time from 2 days to just about an hour. Student Assistant University at Buffalo, the State University of New York February 2023 - May 2023

• Conducted research and presented data analysis using outlier detection methods such as Isolation Forest and DBSCAN to address fraudulent healthcare claims, improving fraud detection accuracy. ACADEMIC EXPERIENCE University at Buffalo, the State University of New York Fraud detection in health insurance claims Anomaly Detection, Sklearn, Seaborn 2024

● Developed a fraud detection system for health insurance using anomaly detection algorithms Isolation Forest and Autoencoder.

● Preprocessed Kaggle’s healthcare provider data of 100,000+ records using binary encoding and standardization.

● Implemented PCA for dimensionality reduction, visualizing outliers on both 2D and 3D scatter plots. Captured 95% of fraudulent cases with Autoencoder by minimizing reconstruction error during training, reducing false positives by 20%. ETL data warehouse project Data Engineering, Tableau, Talend Studio, Oracle Cloud 2023

• Constructed a comprehensive data warehouse for a grocery chain using Talend Studio to efficiently manage the design of fact and dimension tables.

• Implemented a data pipeline for loading data from Excel files into Oracle Cloud, enhancing data accessibility and integrity.

• Integrated Tableau for dynamic reporting, providing actionable insights on region-wise sales, employment rates, and predicting sales forecasts for the next year.

Land mine detection Statistical Clustering, R 2023

• Conducted clustering analysis for land mine detection, utilizing K-means and hierarchical clustering on normalized sensor data.

• Identified patterns in voltage, height, and soil type, revealing potential insights into mine-types. Incorporated dimensionality reduction through PCA, achieving 80% variance explained with clear clustering in a biplot. Fake news classification Classification, Python, Sklearn, Keras 2023

• Developed a Fake News Classifier using Kaggle’s WELFake dataset to combat the spread of false information.

• Enhanced model training by preprocessing techniques like TF-IDF vectorization, stemming, and stop-word removal using NLP.

• Experimented with various machine learning models including Passive Aggressive Classifier, Random Forest Classifier, Decision Tree Classifier, and Linear SVM.

• Achieved the highest model accuracy of 96.9% with the Passive Aggressive Classifier, demonstrating effective model efficacy. SKILLS, COURSES & CERTIFICATIONS

• Tools: Git, VSCode, VBA, Jenkins, JIRA, MS Excel,

• Programming Languages: Python, R, Matlab, MSSQL, PostgreSQL, Azure, Databricks, C, C++, C#, ASP.NET, JavaScript, HTML

• Data visualization and integration: Tableau, PowerBI, Snowflake, Talend Studio

• Certifications: Snowflake, Tableau and Gen AI

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