Adithya Prahasith
Analytics & AI
I'm a Data Science professional with 2+ years of experience in leading high-impact analytics projects across B2B SaaS and E-Commerce. Adept at transforming business needs into actionable insights using Python (ETL, ML), SQL, and BI tools like Tableau and Power BI etc. My recent works includes building RAG-based chatbots and LLMs Fine-tuning. I hold a Master’s in Data Science (GPA: 3.97) from UMass and I’m currently seeking full-time roles to solve business challenges with data-driven solutions.
Work Experience
Data Analyst
Key Achievements:
Testimonials
I would recommend Adithya Prahasith for his exceptional contributions as a Data Analyst in our Analytics team at Airmeet. He demonstrated an exceptional understanding of Sales, Account Management Expansion & Retention Analytics etc. and was instrumental in identifying key trends and driving revenue growth. He is a proactive problem solver, delivering actionable insights and collaborating seamlessly with the team. I found him quick to learn new concepts, hard working and extremely committed. He will be an asset to any organization he gets associated with."
Adithya is amazing with his Data Skills. He has a great Business understanding and has the ability to solve problems by understanding them deeply. He has helped Airmeet to scale to high level with his proactive Analysis and he is a great team person. Amazing person to work with."
Data Analyst
Key Achievements:
Featured Projects
Portfolio Optimization with A/B Testing
To improve user engagement, I conducted an A/B test on two live versions of my portfolio—Version A (project-focused) and Version B (detailed layout with interactive elements). Using GA4 and Google Tag Manager, users were randomly routed and tracked via custom events like resume clicks and scroll depth. Version B outperformed with 62% higher engagement, 31% lower bounce rate, and 41% more interactions, validated using Python and visualized in Looker Studio.
Supply Chain & Operations Analytics
Built an Interactive Tableau dashboard analyzing factory-to-customer shipping routes, product logistics, and customer performance. Uncovers real-time insights to optimize supply chain costs, delivery efficiency, and retention strategies.
Youtube Spam Abuse Analytics Dashboard
This dashboard replicates an internal YouTube Trust & Safety analyst view. It visualizes comment abuse trends, user behavior, and platform-level spam insights. The primary goal was to explore how spam proliferates, which users or videos are most impacted, and how to simulate operational flags like escalation or chronic abuse.
BofA Consumer Complaints Analysis
This project analyzes consumer complaints submitted to the CFPB against Bank of America from 2017 to 2023. It focuses on identifying trends, product-level issues, resolution effectiveness, and regional complaint patterns to recommend improvements in customer experience and operational efficiency.
openFDA Tobacco Reports Analysis
This project analyzes FDA tobacco product complaint reports to identify health risk patterns, forecast future issues, and make the insights accessible via an AI-powered Q&A system using Retrieval-Augmented Generation (RAG). It combines data analysis, machine learning, time-series forecasting, and LLM-based question answering to support public health monitoring and decision-making.
Linkfire Web Traffic Analytics
This project analyzes Linkfire’s web traffic data to identify user engagement trends by geography, content, and device type. It includes Tableau dashboards and data-driven recommendations to improve link click-through rates (CTR) and marketing effectiveness.
Balanced Tree Clothing Sales - Case Study
This project involves SQL-driven analytics for Balanced Tree Clothing, analyzing 15K+ transactions to track product, customer, and time-series sales performance. It includes an automated monthly report script and Tableau visualizations to support business insights and strategy.
Marketing Campaign Response Prediction
Developed classification models (Logistic Regression, XGBoost, Random Forest) to predict customer response to retail marketing campaigns with 88% accuracy. Performed advanced feature engineering and class balancing using ROSE. Key predictors included recency, campaign history, and web activity.
Flight Fare Prediction
Built a machine learning pipeline to predict airline ticket prices using real-time data scraped from Kayak.com. Conducted EDA, preprocessed 2K+ rows of data, and identified key fare drivers like travel class and departure time. Trained & Validated models including Random Forest and SVR, achieving an avg R² score of 87%.
Secure Password Manager Application
Built a desktop application that enables users to securely manage passwords using AES-256 encryption, storing all credentials in an encrypted local JSON database. Implemented features like user authentication, security question verification, password generation, and import/export functionality via a custom Tkinter GUI. Applied cryptographic best practices to ensure Confidentiality, Integrity, and Availability (C.I.A.) principles were upheld.
Technical Skills
Programming Languages
Data Analysis & ML
Business & Analytics
BI Tools
Database & ETL
Design & Development
Professional Certifications
Education
GPA: 3.97/4.0
- Relevant Coursework: Advanced Machine Learning, Artifical Intelligence, Business Analytics & Data Mining, Advanced Mathematical Statistics, Big Data Analytics
- Clubs & Activities: Member of Big Data Club, Gen AI Club
- Served as Graduate Student Assistant for the Admissions Department, helping them maintaining data quality and validation checks for the global universities records
GPA: 3.45/4.0
- Relevant Coursework: Data Structures, Algorithms, Database Systems, Software Engineering, Core Java Programming, Cloud Computing
- Clubs: Google Developer Students Club
Contact
Let's Collaborate
I'm always interested in discussing new opportunities, data challenges, and innovative analytics projects. I'd love to hear from you :)