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Click here or scroll down to respond to this candidateCandidate's Name
PHONE NUMBER AVAILABLE EMAIL AVAILABLE LinkedIn GithubEDUCATIONUniversity of South Florida, Muma College of Business, Florida Aug2022-May 2024 Master of Science in Business Analytics, and Information Systems GPA: 3.93 Amrita School of Engineering, Kerala Jun 2017-Jun 2021 Bachelor of Technology: Electronics and Communication Engineering GPA: 3.87 TECHNICAL SKILLSData Analysis: SQL, Python, R, Excel (VLOOKUP, PivotTables, INDEX MATCH, IF, SUMIF), VBA Data Visualization: Power BI including DAX, Tableau, Qlik Sense, Google Data Studio Database Management: Oracle, Azure SQL, PostgreSQL, MySQL ETL Processes: SSIS, PySpark, AWS DMS, Azure Data Factory, Hadoop Big Data Technologies: Hadoop, Hive, Apache Spark, AWS S3, Azure Data Lake Machine Learning & AI: Scikit-learn, TensorFlow, Regression and Classification models, NLP Programming Languages: C++, Delphi, JavaScriptData Integration: Data Migration, Data Quality Assurance, Data Validation System Administration: System Health Checks, Disaster Recovery, JIRA (Service Level Agreements) Microsoft Office Suite: MS Word, MS Excel, MS PowerPoint Miscellaneous: C, C++, Java.EXPERIENCEEMI Industries through Robert HalfReport Developer Sep 2024-present Developed a Power BI Sales Performance Dashboard to analyze sales growth, customer acquisition, and product profitability, driving better business decision-making for a manufacturing firm. Integrated and cleaned data from CRM and ERP systems (Macola and SAP) using MySQL Studio, SQL queries, and Prophix, ensuring accurate and up-to-date information for robust sales and financial analysis. Designed advanced KPIs and dynamic dashboard reports using DAX formulas to measure monthly sales growth, customer retention, and product profitability. Developed comprehensive financial dashboards covering P&L, Balance Sheet, Revenue, COGS, Net Income, and Customer Profitability, with a focus on trailing twelve months (TTM) for revenue and COGS analysis. Created drill-through capabilities for financial dashboards in Power BI allowing deep analysis of financial and operational data, enabling stakeholders to take informed actions quickly. Leveraged Prophix, MySQL Studio, SQL queries, Excel and Power BI to perform in-depth financial analytics in a manufacturing firm, driving operational efficiency, profitability insights, and improved decision- making across teams. Reported directly to the CFO, delivering insights on revenue trends, profitability analysis, and cost optimization, which improved strategic decision-making and contributed to a 15% increase in operational efficiency. Gradate Research Assistant, University of South Florida Aug 2023-May 2024 Implemented ETL pipelines to extract, transform, and load customer support metrics from multiple sources. Created efficient SQL queries to analyze large datasets, optimizing query performance and reducing response time. Collaborated with stakeholders to define requirements and provide actionable insights through data analysis. Developed real-time performance dashboards in Power BI and Tableau, monitoring agent performance and improving service levels. Ensured seamless data integration and standardization, improving data consistency across the organization. Dish Network TechnologiesSenior Data Analyst Oct 2021-Jun 2022 Developed and deployed a financial forecasting model using Python and SQL, improving forecast accuracy by 20%, enabling more precise budgeting and financial planning. Created a Power BI dashboard integrating data from multiple financial systems, providing executives with real-time insights into revenue, cost trends, and profitability, accelerating decision-making. Designed DAX-powered KPIs to track critical metrics such as gross margins, operating costs, and net income, delivering enhanced clarity on financial performance. Conducted a comprehensive cost optimization analysis, identifying inefficiencies in vendor management and internal operations, resulting in a 15% reduction in overhead costs. Automated financial reporting workflows, streamlining data integration from ERP systems and significantly reducing the manual effort required for monthly reporting. Delivered strategic recommendations to the executive team based on deep financial analysis, contributing to a 10% improvement in resource allocation and operational efficiency. Tata Consultancy ServicesData Analyst Jan 2021-Oct 2021 Streamlined ETL processes using Oracle SQL, reducing data integration processing time by 30%, which improved overall data flow efficiency for business analysis. Managed a financial data warehouse in Oracle 19c, implementing data standardization and cataloging techniques to ensure accurate and consistent financial data for reporting and analysis. Developed and optimized complex SQL queries for efficient financial data retrieval and reporting, enhancing real-time business insights. Built real-time financial dashboards in Power BI and Excel (Pivot tables), providing stakeholders with enhanced visibility into key financial KPIs, budget performance, and other business-critical metrics. Applied data mining techniques to uncover patterns and trends in financial data, significantly enhancing business intelligence and enabling more informed decision-making. Collaborated with finance and business teams to translate data insights into actionable strategies, driving improvements in financial planning and resource allocation.PROJECTSData Extraction and Analysis - Infobae Led data analytics initiative to integrate Marfeel API for content optimization at Infobae, enhancing user engagement by generating relevant news articles based on reader behavior. Developed data-driven recommendation systems using Marfeel data to suggest similar high-viewership articles, resulting in a 20% increase in content click-through rates. Utilized Python and SQL for data extraction, transformation, and loading (ETL) processes, ensuring seamless integration of Marfeel data into Infobae's analytics pipeline. Analyzed user interaction patterns to translate and publish trending content across multiple languages, increasing global readership by 15%. Automated reporting and dashboards in Tableau to monitor content performance metrics, providing actionable insights that guided editorial strategies and improved reader retention. Customer Analytics for E-commerce Developed a customer analytics platform using Tableau, Qlik sense and Excel providing insights into buying patterns and behaviors. Analyzed data from multiple sources, including online transactions and customer feedback. Created interactive visualizations to highlight key trends and opportunities for personalized marketing. Collaborated with marketing teams to use data insights for targeted campaigns, increasing customer engagement. Contributed to a 20% increase in customer retention by identifying key factors influencing customer satisfaction. Troubleshooting Power BI Dashboards Data Integration: Aggregated and cleaned credit card transaction and customer demographic data using SQL, ensuring a unified dataset for analysis. Data Modeling: Built a data model with fact and dimension tables in Power BI, enabling detailed analysis of credit card usage and customer behavior. DAX Calculations: Developed advanced DAX measures to calculate customer lifetime value (CLV), churn risk, and average transaction value. Interactive Dashboard: Designed visualizations for customer segmentation, spending patterns, and geographic analysis, highlighting key trends and insights. Actionable Insights: Identified high-value customer segments and churn risks, leading to targeted marketing strategies and improved customer retention efforts. Predictive Analytics for Online Shoppers Intentions Spearheaded a comprehensive big data project to address declining customer engagement, rising bounce rates, and diminishing revenue in the e-commerce business. Applied Logistic Regression, Decision Trees, and Random Forests to forecast customer intent, informing website design and marketing strategies in the B2C online shopping segment. The analysis suggests that while a Random Forest model excels in predictive analytics within e- commerce. platform with high F1 scores and AUC values. The linear SVC model outperforms overall, especially in accuracy, F1 score, and balanced precision- recall making Linear SVC ideal for handling imbalanced datasets. |
