Machine Learning Data Science

Rohith Peddi

********@********.*** +1-720-***-****

EDUCATION

MS in Data Science May 2025

University of Colorado Boulder GPA 4.0

B.Tech in Electrical and Electronics Engineering May 2021 National Institute of Technology, Warangal GPA 8.12/10 SKILLS

Programming Languages: Python, PySpark, R, SQL, C++ ML/AI libraries: Pytorch, Pyspark, Numpy, Pandas, Matplotlib, Keras, OpenCV, Nltk, Tensorflow, Scikit-learn. Data-Science: Machine Learning Models, Generative AI, Prompt Engineering, Neural Network, MySQL, Tableau, Power BI, Databases, Statistical Analysis, Predictive Analytics, Data Mining, Data Analysis and Visualization, Microsoft Excel, Git

PROFESSIONAL EXPERIENCE

Data Scientist June 2021 - June 2023

ZS Bangalore, India

• Spearheaded the development of a cutting-edge HCP engagement model, ensuring future readiness through personalized micro-segment driven strategies.

• Implemented a two-tier segmentation approach leveraging machine learning algorithms to cluster HCPs based on demographic and engagement data for better targeting system, resulting in a 4.7% sales lift.

• Engineered algorithms for early anomaly detection, monitoring brand performance, promotional activities, and consumer engagement, leading to a 15% return on advertising spend (ROAS).

• Developed a python package that generates Customer Engagement Index for HCPs using engagement data like email opens, clicks, event attendance, etc. which was used across other teams in various applications.

• Worked on item recommendation use cases and achieved top 2% as part of personalization product development.

• Designed and automated an end-to-end pipeline using Apache Airflow, streamlining workflows, and enhancing user interface

(UI) capabilities.

• Leveraged SQL for extracting, transforming, and loading data from diverse sources, ensuring data integrity and accessibility for analysis.

• Analyzed and visualized complex datasets using Tableau, resulting in a 40% reduction in time spent on data exploration and enhanced data-driven insights.

Data Science Intern July 2020 - May 2021

Fusion Technologies Remote

• Developed a robust image to text classification model specifically tailored for vaccination record images. Achieved an impressive accuracy rate of 80%, showcasing the model's effectiveness in accurately extracting text data from diverse vaccination record images.

• Designed and implemented a face recognition system to address security needs using real-time generated image data. Developed the system to accurately identify and verify individuals based on facial features, enhancing security protocols and access control measures.

PROJECT EXPERIENCE

Video Transcription and Summarization using NLP.

• Leveraged Large Language Models (LLMs) such as GPT-3 or similar architectures to summarize complex university lectures. Utilized Natural Language Processing (NLP) techniques to process lecture transcripts and generate concise summaries that capture key concepts and information.

• Implemented Bidirectional Encoder Representations from Transformers (BERT) and LangChain models for analyzing and processing lecture video content. Conducted an in-depth evaluation for models using a dataset comprising 1200 lecture videos. Solar Irradiance forecasting

• Used various Deep learning models like CNN, LSTM, Ensemble models to predict the future solar irradiance.

• Achieved R2 scores of 80% and 82% for two different locations. Product recommendation engine

• Designed and implemented a product recommender system leveraging Natural Language Processing (NLP) techniques combined with product metadata and customer transaction data.

• Implemented advanced features including user-to-item and item-to-item similarity metrics, user-item interaction counts, and popularity trends over specific time windows (e.g., last 7 days, last 30 days).

• Trained LightGBM and CatBoost ranking models to predict customer preferences and generate accurate product rankings.

• Achieved an impressive 92% accuracy in recommending final products to customers, optimizing for relevance and likelihood of purchase based on historical data and real-time user behavior. ACHIEVEMENTS

• National Engineering Olympiad Awardee (NEO 3.0).

• Achieved 3rd position in NLP based recommendation competition.

• Achieved top 10 percentile in JEE 2017.

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