Business Analyst Power Bi

Navya Sanniboina

****************@*****.*** +1-940-***-**** linkedin.com/in/navya-s-6a784b34a Business Analyst with 2 years of experience and a strong academic foundation including a Master’s in Business Analytics and an M.B.A in Finance. Skilled in uncovering insights through data storytelling, statistical modeling, and financial analysis to inform strategic decision-making. Adept at using tools like SQL, Python, Excel, Power BI, and Tableau to drive process improvement, fraud detection, and investment evaluation across finance, public sector, and marketing domains.

EDUCATION

University of North Texas, Denton, TX GPA: 3.7/4.0 Master of Science in Business Analytics Aug 2023 – May 2025 Relevant Coursework: Predictive Analytics, Data Mining, Database System, Model based BI, Data Visualization, Information Systems, Statistical Analysis, Business Process Analytics Jawaharlal Nehru Technological University, Hyderabad, India GPA: 85% Master of Business Administration in Finance Jul 2018 – May 2020 Relevant Coursework: Business Decision Process, Statistics, Information Systems, Auditing, Accounting, Financial Analysis, Supply Chain Analytics

Osmania University, Hyderabad, Telangana, India GPA: 85% Bachelor of Commerce in Computers Jul 2015 – May 2018 Relevant Coursework: finance, Computer Applications, Accounting, Economics, Business Law SKILLS

• Languages & Technologies: Python, SQL, R, AWS, MySQL, SAP, PostgreSQL, MongoDB, GCP, MATLAB, XML, XSLT

• Statistical & Financial Analysis: Descriptive Statistics, ETL workflows, Financial Forecasting (ARIMA/SARIMA), Regression Analysis, Hypothesis Testing, Portfolio Optimization, Risk & Return Evaluation, Fraud Investigation, Time Series Analysis

• Data Visualization & Analytics: Tableau, Power BI, MS Excel, Wolframe Mathematica, KNIME, Machine Learning

• Frameworks/Libraries: Scikitlearn, OpenCV, Numpy, Pandas, matplotlib, plotly, seaborn, Keras, Pyspark, Streamlit EXPERIENCE

Graduate Research Assistant Texas, USA

University of North Texas Jan 2024 – May 2025

Conducted in-depth analysis of large datasets using Python SQL to identify key market research trends, uncovering consumer behavior patterns and product adoption rates.

Designed and implemented advanced statistical models, including regression analysis and clustering techniques, to evaluate consumer purchasing behaviors and decision-making influences.

Created visual dashboards using Power BI and Tableau to present insights clearly and support business decisions Business Analyst – Operations & Strategy Telanagana, India Freelance March 2022 – April 2023

Managed hostel operations using Excel and Power BI to monitor occupancy, revenue, and expenses; reduced operational costs by 20%.

Applied business analytics to optimize pricing, reduce overdue payments by 40%, and enhance resource allocation.

Conducted customer preference and competitor analysis using Excel trend plots and Power BI visuals to adjust pricing and launch targeted marketing campaigns, resulting in increased occupancy during off-peak seasons. Operational Analyst Hyderabad, Telangana, India

Wells Fargo Feb 2021 – Feb 2022

Led fraud detection analysis across high-volume customer accounts data, reducing false positives by 18% through pattern recognition and anomaly detection in transactional datasets.

Analyzed over 1M transactions using SQL and Excel to identify suspicious activities and emerging fraud trends across credit card, loan, and bank operations.

Collaborated with cross-functional teams (risk, compliance, legal) to validate over 500+ claims monthly, improving resolution turnaround time by 22%.

Created dashboards and visual reports in Excel and Power BI to highlight key metrics such as claim resolution time, fraud occurrence rates, and recovery percentages.

Recommended preventive strategies by identifying recurring fraud types, helping reduce repeat cases and strengthen internal controls.

Supported monthly audit reporting and ensured timely documentation for compliance teams and financial regulators. PROJECTS

• Sentiment Analysis on Social Media: Built a sentiment classification pipeline using TF-IDF and models like Random Forest, SVM, and Logistic Regression; Random Forest achieved 41% accuracy. Enhanced sentiment detection with VADER for informal text. Extracted insights across geography, time, and hashtags to inform brand perception and marketing strategies.

• Analyzing Certifications of NYC Public Sector Employees: Analyzed NYC Civil Service Certification data to uncover hiring trends and workforce planning gaps. Engineered features and applied SARIMA(2,1,1)(1,1,1)[12] for seasonal forecasting. Revealed key patterns in certification expiration and agency hiring behavior to guide staffing policy.

• Risk Return Analysis of Pharma Stocks: Performed 5-year financial analysis of top pharmaceutical firms. Calculated returns, volatility, and beta; applied portfolio theory to assess risk-return tradeoffs. Identified Divi’s Labs as low-risk and Sun Pharma as high-growth, supporting informed investment decisions.

• Smartphone Market Analysis Dashboard: Built an interactive Tableau dashboard analyzing 980 smartphone models across 26 features and 10+ brands. Uncovered that models with 5G support, 6+ GB RAM, and 5000mAh+ batteries received 30% higher customer ratings. Revealed brand positioning—Xiaomi captured 40% of the budget segment, while Apple dominated the premium tier. Delivered insights to guide pricing and feature strategy using data-driven visual storytelling.

• Customer Churn Prediction Using Machine Learning: Built a predictive model using a telecom dataset with 7,043 customer records to classify churn. Cleaned and encoded 20+ features, handled class imbalance, and trained models including Logistic Regression and Decision Trees. Achieved 80%+ accuracy and identified key churn drivers like contract type, tenure, and monthly charges—enabling proactive retention strategies.

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