Data Science Machine Learning

Vaishnavi Sundaraganapathi

Ó 517-***-**** R ********@***.*** linkedin.com/in/vaishnavi-s-b09227168/ Education

Master of Science, Data Science, Michigan State University GPA: 4.0, Expected: May 2026 BE, Information Science and Engineering, Ramaiah Institute of Technology GPA: 3.6, Graduated: Aug 2022 Summary

As a data science student at the Michigan State University with 2 years of industry experience, my passion for data science is fueled by unwavering hard work, self-motivation, and an insatiable curiosity. I enjoy the challenges of building innovative products and exploring advanced technologies that have the potential to make a meaningful impact. With my boundless resilience and determination, I am confident that I can achieve far greater achievements than I currently envision. Skills

Machine Learning & Statistics: Regression Modeling, Time Series Forecasting, Statistical Modeling, Feature Engineering, XGBoost, RandomForest, NLP, LLMS Data Science Tools: Python (NumPy, Pandas, Data cleaning, Exploratory Data analysis, scikit-learn, PyTorch), ARIMA, SARIMA, LSTM

Data Engineering: ETL Pipeline Development, Data Quality Management, Real-time Data Processing, SQL, NoSQL Big Data & Cloud: Azure Databricks, PySpark, Azure Event Hubs, CosmosDB, GCP Business Software Tools: Microsoft Excel, Word, PowerPoint, Git, Azure DevOps, Agile Softskills: Professional communication, self-motivation, hard work, Curiosity driven Experience

BOEING Data Engineer — Software Engineer July 2022 – Aug 2024

• Developed and implemented machine learning models for real-time flight status monitoring, processing over 5 million data points daily using advanced statistical analysis and predictive modeling

• Led cross-functional teams in developing automated data processing workflows, resulting in 98% reduction in deployment time and significant cost savings

• Created sophisticated data pipelines through gitlab yml files and Databricks integrating multiple data sources for flight analytics, implementing robust quality control measures

• Conducted thorough statistical analysis and model validation, ensuring accuracy in flight prediction algorithms while maintaining code qualities and standards

• Built a project for estimating the ETA of flights by using the API that we built and by using other API’s available within Boeing’s Developer tools resources within 24 hours

DUTCHVIEW Data Science Intern Feb 2022 – July 2022

• Performed comprehensive statistical analysis on multiple datasets, developing interactive dashboards for tracking key performance metrics

• Implemented machine learning models (XGBoost, Neural Networks) for customer prediction, focusing on model interpretability and business impact

Projects

NASDAQ Stock Prediction Using Time Series Analysis Python, ARIMA, SARIMA December 2024

• Developed and implemented multiple time series forecasting models for stock price prediction, incorporating sentiment analysis

• Created interactive visualization tools for financial data analysis, enabling clear communication of complex patterns

• Deployed model via Streamlit for real-time predictions: https://cmseproject-6hfzstmwzbeyfqcphswtlq.streamlit.app/ Advocate SDXL, ChromaDB, Langchain, NLP February 2025

• Created an agentic research assistant which researches about the company and builds campaigns and images for every campaign to make interactive advertisements

• Explored image building agents like Stable diffusion, midjourney and SDXL for image generation Supply Chain Demand Prediction Python, Machine Learning, LSTM June 2022

• Implemented advanced statistical models and LSTM networks for demand forecasting, achieving 25% improvement in accuracy

• Conducted comprehensive data analysis and feature engineering to optimize model performance

• Effectively communicated technical findings to conferences to showcase the results which was then published at Grenze publications Music Emotion Recognition System XGBoost, Random Forest December 2021

• Applied advanced feature engineering techniques to extract and analyze audio characteristics, developing a robust classification model

• Implemented and compared multiple machine learning algorithms for emotion detection, optimizing for accuracy and performance

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