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PHONE NUMBER AVAILABLE EMAIL AVAILABLE LinkedIn GitHub PortfolioData Scientist with a Masters in Data Science and three years of experience; specializes in machine learning and innovative analytics. Proven ability to deliver high-impact, data-driven solutions, taking full ownership of projects to drive business success while maintaining the highest ethical standards and a commitment to continuous improvement. EducationUniversity of DelawareJNTUA KalikiriMaster of Science in Data ScienceBachelor of Technology(B.Tech)Computer ScienceCGPA: 4/4GPA:8/10Feb 2023 - May 2024June 2016-May 2020Professional ExperienceData Science Research Assistant, University of Delaware June 2023 - May 2024 Developed a targeted image analysis model using ResNet-101, focused on detecting early signs of osteoarthritis in knee X-ray images, achieving 85% accuracy and 90% sensitivity in identifying joint degeneration markers. Authored a research paper detailing sophisticated data extraction and classification techniques for UNFCCC participants, accompanied by detailed data visualizations using Power BI to identify complex data patterns. Developed a Python-based ETL pipeline to transform 162 inconsistent text and image PDFs into structured CSV files with 95% accuracy, employing advanced data cleaning methods to maintain data integrity. Created an advanced NLP-driven text classification model that outperformed traditional language models (LLMs), achieving a 92% classification accuracy rate in sorting past 34 years of important UNFCCC attendee data. Data Scientist Trendlyne.com Dec 2021 - Jan 2023 Developed a machine learning model using Gradient Boosted Decision Trees on Amazon Web Services - Sage Maker to classify stocks as ASM(Surveillance Measure), achieving 83% accuracy and enhancing DVM scores reliability. Automated a stock portfolio analysis tool and interactive dashboards, enhancing data analytics and data visualization, resulting in a 37% increase in monthly active users through improved clustering techniques. Led Agile development of financial web pages to integrate real-time stock feeds and implement statistical analysis for performance snapshots, earnings call podcasts, and business intelligence insights; optimized security protocols with email OTPs, boosting 18% increase in site traffic, instrumental in securing $2 million in funding. Implemented MLOps best practices by utilizing Git for version control to deploy 186 high-performance REST APIs, managing 700 million monthly hits, and optimizing data mining on large datasets to enhance data solutions for 18 Large Stock Brokers B2B client engagement.Junior Data Scientist CovalenseDigital Sep 2020 - Dec 2021 Developed and deployed computer vision algorithms (yolo v3, OCR-PyTesseract) with TensorFlow-Keras, to digitalize handwritten Purchase Orders with 92% accuracy, enhancing operational efficiency and leveraging Docker for deployment. Designed and implemented a cross-platform web UI, automating database migration files and enhancing team collaboration, resulting in a 90% increase in development speed and showcasing strong SQL skills. Led the implementation of a payments module for Africell in Angola, optimizing transaction speeds by 30% and ensuring 99.9% accuracy for 10 million users, utilizing NumPy and Pandas for data manipulation. Earned the Junior Champion and SPOT Award for Python and Machine Learning skills, and 'Best Performer' for CSMART Payments module development at CovalenseDigital in 2021. Skills Programming Languages: Python, SQL, C. Data Analysis & Machine Learning: Pandas, NumPy, EDA - Data visualization (Matplotlib, Seaborn, Power BI, Tableau), Classical Machine Learning, Deep learning, NLP, Computer Vision, Statistical methods, XGBoost, LightGBM, System Design, Scikit-Learn, Generative Models (GMM, GANs), Predictive Modeling, Image Recognition, Forecasting, Sentiment Analytics, Optimization Techniques, Feature Engineering. Data Science Processes: A/B testing, ETL, Data Cleaning, Time Series, Recommender Systems. Software & Tools: Django (Full Stack), MySQL, Git, GitHub Actions, CI/CD Pipelines, JIRA, REST APIs, Beautiful Soup, Big Data (PySpark), Excel, Linux, TensorFlow, PyTorch, MLFlow, Streamlit, Jupyter Notebooks, R Studio. Outstanding verbal and Oral communication and collaboration skills; proficient in distilling complex technical information for non- technical stakeholders; analytical and critical thinking skills. Projects [GitHub] Signature Verification: Implemented deep learning techniques (Computer Vision), specifically Siamese CNN, for Signature Verification, achieving a significant 73% accuracy in differentiating genuine and forged signatures. Generate Anime Images using GANs: Applied Generative AI(GenAI) - GANs to create high-fidelity anime images, optimizing model performance during training. USA Voter Turnout Analysis and Prediction: Analyzed voter demographics using classical ML to predict USA Voter Turnout at county level with R-squared of 0.69, assisting in policy decision-making. Educational Project Approval Prediction for DonorsChoose.org: Predicted proposal approvals using NLP Techniques and Machine Learning Algorithms achieving a test accuracy of 0.66. |
