Data Analyst

Prajwal Chidri Prashanth

919-***-**** *************@*****.*** LinkedIn

PROFESSIONAL SUMMARY

A data-driven problem-solver with a passion to unlock insights hidden within data, well-versed in translating complex business challenges into actionable insights through strategic data analysis. Proficient in Pyspark, SQL, Python, Tableau, Power BI, and other data analysis tools, skilled in communicating with stakeholders and driving data-driven decision-making for the company. EDUCATION

Master of Science in Business Analytics, University of Illinois, Chicago, IL [GPA 3.77/4.00] May 2024 Bachelor of Engineering, Ramaiah Institute of Technology, Bengaluru, India [GPA 3.3/4.00] Jul 2021 WORK EXPERIENCE

Data Engineer, Alorica, Remote, USA Aug 2024 – Present

• Designed delta table structures with optimized storage and indexing strategies, decreasing storage costs by 15% and improving query performance for downstream analytics.

• Built and optimized distributed data pipelines using Spark and Azure Synapse, reducing query execution time by 40%, enabling faster insights for real-time business decisions.

• Developed an automation framework to detect and prioritize data quality defects, enabling targeted deep dives and reducing manual QA efforts by 10+ hours per cycle, while ensuring high data integrity.

• Collaborated with cross-functional teams to design Delta tables, reducing downstream processing time by 40% and enabling faster, more accurate reporting by aligning data with client-specific business rules. Data Analyst, Fermilab, Chicago, USA Jan 2024 – May 2024

• Developed a Power BI data model to visualize and analyze the organization’s procurement process, reducing average purchase order cycle time by 20%, thereby enhancing operational transparency and strategic decision-making.

• Utilized IQR for anomaly detection in procurement data, enhancing integrity and reducing errors by 30%, improving decision-making.

• Implemented dynamic Power BI dashboards to migrate the Procurement Dashboard from Excel, resulting in increased accessibility to real- time financial insights and a 15% improvement in budget allocation accuracy.

• Utilized OBIEE for detailed spend analysis in procurement, identifying key trends in supplier pricing and order patterns, which enabled a 5% cost reduction through optimized contracts and demand management. Graduate Data Analyst, University of Illinois, Chicago, USA May 2023 – May 2024

• Developed centralized tables serving as a single source of truth for diverse departments, facilitating seamless ad-hoc analysis and minimizing conflicts arising from data discrepancies.

• Created an interactive Tableau dashboard to visually depict student enrollment trends and dropout rates, empowering course advisors with actionable insights to optimize retention strategies and improve student success metrics.

• Improved the enrollment rate by 20% for courses that had a higher drop off rate by leveraging course survey data, and feedback rating.

• Leveraged linear and logistic regression to predict dropout probabilities from GRE scores, informing targeted marketing strategies for improved student retention.

Data Analyst, Micro Technoid Private Limited, Bengaluru, India Aug 2021 - Jul 2022

• Developed dashboard to analyze warehouse metrics, optimizing inventory levels and increasing capacity utilization by 20%.

• Designed intuitive, interactive dashboards that illustrate KPIs, key findings, forecasts and provide detailed monthly and quarterly results.

• Transformed data using SQL based on analysis of use cases to drive the underlying datasets for dashboards.

• Extracted data (128 million rows) from multiple sources, cleansed for operational needs and loaded into target databases for analysis. ACADEMIC PROJECTS

Data Engineering Project

• Established a Kafka-based streaming system on Docker to capture real-time financial data, stored in AWS S3 for scalability.

• Performed data cleaning using Python, removing null values and duplicates, and conducted exploratory and comparative analysis of financial indexes.

• Implemented AWS Glue for ETL processes, transforming and loading data into AWS Athena for advanced querying and analytics.

• Developed SQL queries within Athena to extract insights, supporting strategic decision-making based on market trends. Lending Club Machine Learning Project

• Delved into the financial records of over 100,000 borrowers from Lending Club, to uncover trends and patterns.

• Employed advanced machine learning techniques such as Logistic Regression, KNN, Decision Tree and Boosting to predict loan default rates with remarkable accuracy.

• Achieved a high-precision rate of 95.3% by harnessing the power of Random Forest and XG Boost models. Exploratory Data Analysis on American Airlines

• Analyzed 3.7 million flight records and 50 key variables to evaluate and optimize American Airlines' performance in FY2021, creating insightful dashboards that identified key drivers behind a 12.12% delay rate compared to 11 competitors.

• Recommended to reduce American Airlines' flight delay rates by 4.8%, based on the root causes and key contributors to flight delays. SKILLS

Competencies: Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN, Bagging and Boosting Tools: Tableau, Power BI, Kafka, Docker, AWS, MySQL, MS-Excel Programming Languages: Python, Pyspark, R, SQL

CERTIFICATIONS

Azure Fundamentals – 2023

Microsoft Certified: Power BI Data Analyst Associate – 2023 Google Data Analytics – 2023

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