Data Analyst Quality Assurance

Yudhishna Kuppala

Jersey City, NJ ***** ****************@*****.*** 408-***-**** Linkedin

PROFESSIONAL SUMMARY

Results-driven Data Analyst with 4 years of experience, specializing in predictive analytics, NLP, and AI-driven solutions. Skilled in building and fine-tuning ML models like Random Forest, Logistic Regression, and NLP-based recommendation systems, ensuring data quality assurance through automated regression testing, canary testing, and ETL validation. Proficient in big data technologies such as Apache Spark, PySpark, Hive, Snowflake, and cloud-based data processing using AWS (S3, Lambda, Glue, RDS, QuickSight) to develop scalable data pipelines. Experienced in real-time data streaming and batch processing, optimizing business operations through ML-driven insights, Tableau dashboards, and automation at Amazon. Research contributions include AI-driven fashion image captioning (OutfitVision AI), demonstrating expertise in computer vision and NLP EDUCATION

Yeshiva University, Katz School of Science and Health New York, NY Master of Science in Data Analytics and Visualization Sep 2023-Dec 2024 Miracle Educational Society Group of Institution Visakhapatnam, Andhra Pradesh Bachelor of Technology in Computer Science May 2019 CERTIFICATIONS & SKILLS

Certifications: AWS Cloud Practitioner, AWS Solutions Architect Associate, AWS Academy Data Engineering, Data Science – PivotalSoft, Java & Web Technologies – Code Brains

Methodologies: EDA, ETL

Big Data Technologies: Hadoop, Spark, Kafka, HBase,MongoDB,Hive Programming Languages: Python, Java, SQL,C,Unix/Linux Shell Scripting,HTML,CSS ML Frameworks: TensorFlow, PyTorch, Scikit-learn,Keras Cloud & Tools: AWS (S3, Lambda, Glue, RDS,Quicksight), Tableau, Power BI, Microsoft Office Suite,MS Excel PROFESSIONAL EXPERIENCE

ZSANALYTICS, INC

Machine Learning Intern June 2024 - Aug 2024, New York

● Developed and optimized ML models, including Random Forest and Logistic Regression, achieving 80% accuracy by analyzing key features like rolling purchases and regional purchase rates, leading to improved customer engagement and targeted marketing strategies.

● Built NLP-powered recommendation systems using TF-IDF, cosine similarity, and embeddings, enabling personalized experiences by identifying the top 5 most relevant sentences and customizing book recommendations based on user interests.

● Fine-tuned GPT models via Hugging Face for advanced personalization, showcasing expertise in Natural Language Understanding (NLU) and enhancing recommendation capabilities for targeted content delivery. Amazon

Data Analyst June 2021 - July 2023, Hyderabad

● Improved program effectiveness for Quality Analysts and Subject Matter Experts by implementing outlier management strategies, enhancing underperforming employee efficiency through continuous improvement initiatives and data-driven decision-making, resulting in a 95% improvement in underperforming employee performance.

● Built Tableau dashboards to analyze and communicate insights, identifying trends, patterns, and outliers to support targeted coaching and development for improved employee performance.

● Designed and delivered customized training programs, leveraging diverse instructional methods and data insights to optimize engagement and enhance the performance of underperforming employees. Subject Matter Expert March 2020 - June 2021, Hyderabad

● Trained new associates through IDS & Training (GO-AI), by integrating machine learning-driven insights and structured SOPs, ensuring compliance with metrics while enhancing skills and operational readiness.

● Conducted fulfillment analysis and Gemba sessions, driving performance improvements, reporting key metrics, and implementing pilot projects to optimize workflows, resulting in a 93% improvement in associate metrics. Quality Analyst May 2019 - March 2020, Hyderabad

● Enhanced warehouse management with machine learning models, using standard operating procedures to refine processes, optimize stowing annotations for Amazon Robotics (AVOC), and drive operational improvements.

● Improved automated workflows in pilot projects, applying machine learning iterations and analytical insights to boost efficiency and exceed quality standards.

Miracle Software System

Software Development Intern May 2018 - June 2018, Visakhapatnam

● Built a dynamic web application using the MEAN stack, implementing responsive design for seamless accessibility across devices.

● Collaborated on MEAN stack and Ionic framework integration, enhancing development efficiency in a team-driven environment. Research & Publication

OutfitVision AI Python Pytorch HuggingFace(Submitted for review, awaiting approval in March 2025) September - December 2024

● Developed an AI-driven fashion image captioning system using BLIP and CLIP, leveraging computer vision and NLP to generate accurate, context-aware clothing descriptions while ensuring semantic alignment with CLIP’s Vision Transformer (ViT-32).

● Designed a scalable data pipeline for processing large-scale fashion datasets, optimizing model training with BLEU, ROUGE, METEOR, CIDEr, and SPICE metrics to enhance caption accuracy while reducing computational costs through mixed-precision training. PROJECTS

Predictive Analysis of Book Preferences TensorFlow Python NLP April 2024 - May 2024

● Engineered and implemented machine learning models, including Logistic Regression, Random Forest, Neural Networks, and Stacking Ensemble, to predict genre popularity and book award likelihood from a comprehensive book dataset.

● Applied advanced techniques in feature engineering, data preprocessing, and natural language processing (NLP) to derive actionable insights, ensuring high accuracy and model interpretability for decision-making. Real Estate Sales and Fire Safety Compliance AWS Python Tableau January 2024 - May 2024

● Developed a scalable ETL pipeline utilizing AWS services like S3, Lambda, Glue, and RDS to seamlessly integrate and transform data from multiple sources, including CSV files and scheduled API calls. Additionally, facilitated efficient data collection and processing.

● Connected RDS to Tableau desktop for interactive dashboards and comprehensive analysis, enabling detailed insights into Real Estate Sales and Fire Safety Certification data for informed decision-making. Data-DrivenTraffic Safety Analysis & Visualization Tableau Prep Tableau Desktop March 2024 – May 2024

● Designed interactive Tableau dashboards to analyze NYC traffic collision data, integrating multiple datasets to uncover key safety insights and trends.

● Applied statistical analysis to segment data by borough, crash time, and involved parties, identifying patterns to support policy recommendations and improve road safety.

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