Ruchitha Gajawada

Experience

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  Smart Bridge Solutions Private Limited

  Apr 2019 - Jun 2019

  Intern

  Experienced in IoT, cloud computing, and device integration. Contributed to POC development for smart fishponds and intruder detection using NodeMCU and Raspberry Pi.

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  International School of Engineering (INSOFE)

  Sept 2019 - May 2020

  Data Science Trainee

  Specialized in analytics and machine learning methodologies, gaining practical experience in advanced data analysis techniques and their real-world applications.

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  Discover Financial Services

  Aug 2020 - Jul 2022

  Data Analyst

   Developed a student performance prediction model using Flask & Python, achieving 90% accuracy and improving graduation rates. Built scalable data pipelines for data ingestion, transformation, and model training, improving processing efficiency by 30%. Designed a spam email classifier (97% accuracy, 100% precision) using NLP techniques (NLTK, Naive Bayes), deployed on AWS. Created a movie recommendation system using NLP (Bag of Words, Cosine Similarity), improving user engagement by 15%. Developed interactive Tableau & Power BI dashboards for key performance metrics, increasing data accessibility by 30%. Show less

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  Lamar University

  Sept 2023 - May 2024

  Data Analyst - Teaching Assistant

   Designed and developed AI-driven data analytics pipelines, improving data processing efficiency by 40%. Created machine learning models for predictive analytics, achieving 85%+ accuracy across multiple projects. Supported students in developing data pipelines, model optimization, and hyperparameter tuning using Linear Regression and Random Forest Regressor. Developed and optimized SQL queries, reducing query execution time by 50%. Built and deployed data visualization dashboards using Power BI, increasing accessibility of insights by 30%. Conducted sentiment analysis on real-world datasets, helping businesses improve customer experience by 25%. Show less

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  NBCUniversal

  Jul 2024 - Jan 2025

  Data Analyst

   Developed and deployed predictive models on AWS, improving model accuracy by 20%. Built a 92% accurate predictive model for bank deposit marketing using Python and sci-kit-learn, increasing conversion rates. Applied SHAP values for customer segmentation, reducing unwanted calls by 96% and enhancing marketing efficiency. Optimized machine learning models with hyperparameter tuning & dimensionality reduction, reducing computational costs by 25%. Implemented Random Forests and Gradient Boosting techniques, achieving an average of 90% accuracy across multiple projects. Conducted EDA on structured/unstructured data, uncovering key insights that reduced false negatives by 15%. Show less