Jaivinay Gudiveka

Graduate Teaching Assistant – USF • Assisting in teaching the Data Analytics for Business course with ~100 undergraduate students • Grading assignments and projects, ensuring fairness and timely evaluation • Supporting lab sessions, including statistical and analytical exercises • Guiding students with doubt clarification and concept reinforcement • Providing assistance during practical sessions to support hands-on learning.

• Built interactive Power BI dashboards integrating SQL and Python outputs to track KPIs (attendance, grades, engagement) in real time; experimented with Generative AI (LangChain, OpenAI API) to automate FAQs and streamline student support. • Developed ML models (classification & regression) to predict student performance trends and recommend personalized study support, improving early detection of at-risk students by 25%. • Architected a centralized PostgreSQL data warehouse using star schema, enhancing reporting accuracy and enabling leadership-level insights through Power BI dashboards, saving 200+ faculty hours annually. • Automated data extraction using Python reducing manual reporting effort by 50% and enabling faster analytics turnaround.

• Designed, built, and maintained data-driven software solutions using Python and SQL, supporting analytics, reporting, and machine learning use cases across enterprise applications. • Developed and optimized ETL/ELT pipelines and analytical datasets on Snowflake and cloud platforms (AWS), enabling reliable data ingestion, transformation, and downstream analytics. • Performed exploratory data analysis (EDA), feature engineering, and data validation on large structured datasets to support forecasting, classification, and performance analysis initiatives. • Built and evaluated statistical and machine learning models (regression, classification, time-series forecasting), applying appropriate metrics and validation techniques to support data-driven decision-making. • Implemented data quality checks, monitoring, and root-cause analysis using SQL and Python, improving accuracy, consistency, and reliability of business-critical datasets. • Collaborated closely with product managers, data scientists, data engineers, and business stakeholders to translate requirements into scalable data pipelines, analytical models, and actionable insights. • Contributed to process automation and optimization, streamlining data workflows and reducing manual effort, while following Agile development practices and maintaining clear technical documentation.

• Worked on data cleaning and basic automation tasks using Python and Excel to support internal reporting. • Created simple dashboards and visual reports using Power BI and Excel to present project metrics. • Used Microsoft Word and PowerPoint to document processes, create internal guides, and prepare presentations. • Logged tasks, bugs, and updates using Jira and collaborated with team members in Agile sprints. • Gained hands-on experience with version control using Git and GitHub for tracking code changes. • Supported senior engineers by assisting in test cases, debugging, and analyzing client-side data issues.

• Contributed to data-driven IT and Business Intelligence solutions for client projects by using Python and SQL to support data processing, reporting logic, and backend analytics aligned with documented business requirements and reporting objectives. • Developed and maintained data workflows to extract, transform, and prepare data from databases and flat files, supporting analytics, reporting, and BI use cases across multiple client engagements and operational teams. • Created and maintained structured datasets, summary tables, and reporting views used for dashboards, periodic reports, and operational insights, enabling stakeholders to monitor performance and make informed decisions. • Performed data validation, debugging, and exploratory data analysis (EDA) to identify data quality issues, investigate inconsistencies, and improve the accuracy, consistency, and reliability of analytical outputs.

• Developed mini-projects using Java and Python, focusing on basic automation, file handling, and data structures. • Gained hands-on experience with SQL for querying and managing relational databases. • Practiced version control and team collaboration using Git and GitHub on project assignments. • Performed basic data analysis and reporting using Excel and Python libraries. • Improved professional communication skills by participating in team discussions and project reviews. • Used Microsoft Office tools for documentation, presentation, and reporting tasks during the internship.