• Email: kmahali2@asu.edu
• Phone: (623) 230-9825
• LinkedIn: LinkedIn Profile

EDUCATION

• Masters in Data Science, Analytics and Engineering (Computing and Decision Analytics)	December 2025
  Arizona State University (GPA 3.78/4)	Tempe, USA

Coursework: Data Mining, Statistical Machine Learning and Statistics for Data Analysts

• Bachelor of Technology in Mechanical Engineering with Honors	July 2022
  SASTRA University (GPA 3.6/4)	Thanjavur, India

Coursework: Programming in C, Object-Oriented Programming in C++, Engineering Mathematics, Statistical Methods and Resource Management

TECHNICAL SKILLS

• Programming: Python (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, spaCy, NLTK), Java, SQL (MySQL, PostgreSQL).
• Data Wrangling: Web scraping (BeautifulSoup, Selenium), data cleaning, preprocessing, and transformation.
• Visualization and Analysis: Tableau, Microsoft Excel, Exploratory Data Analysis (EDA), regression models, and time series analysis.
• Statistical Techniques: Hypothesis Testing, Bayesian Inference, Bootstrapping, and Statistical Inference.

PROJECT EXPERIENCE

• Energy consumption forecasting

  January 2024 – May 2024

  • Engineered a time-series predictive model using XGBoost and deep learning (LSTM), achieving a 15% improvement in accuracy.
  • Identified $223,110 in potential annual savings through quantitative analysis of energy inefficiencies across 24 buildings, contributing to 30% of energy costs.
  • These findings supported strategic decision-making in energy optimization, reducing operational costs.
  • Related Resources
    • Energy consumption forecasting

• Spotify Music Popularity Analysis

  October 2024 – December 2024

  • Analyzed Spotify data using feature engineering, statistical tests, and ML models (84.3% accuracy) to predict popularity and classify moods.
  • Identified 5 emerging artists with a 30% stream growth potential and optimized playlists for 15% higher engagement.
  • Insights facilitated improved user engagement strategies for the Spotify platform.
  • Related Resources
    • Spotify Music Popularity Analysis

• Text and Sentiment Analysis of Political Rally Speeches

  May 2024 – June 2024

  • Executed sentiment analysis on over 30 rally speeches (18,000 tokens each), leveraging complex data sets and quantitative analysis using Python (NLTK, SpaCy).
  • Applied data visualization techniques to extract sentiment trends and word frequencies, optimizing summarization using BART and parallel processing to reduce summarization time by 30%.
  • Findings contributed to better understanding of public sentiment trends in political contexts.
  • Related Resources
    • Text and Sentiment Analysis of Political Rally Speeches

• Optimizing Credit Card Fraud Detection

  January 2024 – May 2024

  • Designed and built a credit card fraud detection system using quantitative analysis on 280,000 transactions, applying modeling techniques (RandomForest, neural networks, logistic regression).
  • Improved detection accuracy by 20% and decreased false positives by 15% through data manipulation and feature engineering with Python.
  • Results supported fraud mitigation strategies, reducing financial risks for credit providers.
  • Related Resources
    • Optimizing Credit Card Fraud Detection

• Sentiment Analysis with DistilBERT on App reviews

  May 2024 – July 2024

  • Constructed and implemented a sentiment analysis model to evaluate over 6,000 user reviews from five productivity apps by deploying the Google Play Scraper API.
  • Optimized preprocessing for efficient CPU performance and implemented the model using PyTorch, achieving a 15% improvement in sentiment classification accuracy and identifying key app features impacting user satisfaction.
  • Related Resources
    • Sentiment Analysis with DistilBERT on App reviews

PROFESSIONAL EXPERIENCE

• Cognizant

  October 2022 – December 2023

  Data Analyst/Programmer Analyst | Chennai, India

  • Optimized processing of 600K credit score and insurance records, reducing processing time by 70% using Python and SQL, demonstrating technical proficiency, attention to detail, and problem-solving
  • Presented actionable insights from data analysis to stakeholders using data visualization tools like Tableau, enhancing data driven decision-making efficiency by 25% and aligning solutions with organizational objectives
  • Conducted performance validation on APIs leveraging Java, Selenium, and SQL, ensuring functionality and performance through Agile methodology and cross-functional collaboration
  • Spearheaded automation testing for various websites, reducing testing time by 85% and identifying 35% more defects, showcasing efficiency and innovation
  • Mentored associates in SQL and Python, enhancing team productivity by 20% through collaboration and technical training