I started my career as a Data Analyst at LatentView Analytics, where over nearly 3 years I built a deep analytical foundation — mastering clickstream analysis, search engine optimization data, and ETL engineering. By the end, I'd been promoted to Senior Data Analyst and led a team that achieved a 60% reduction in ETL processing time through automation and query optimization.
That data engineering fluency became the launchpad for my next move: product. At OYO, I spent 2+ years as a Product Analyst → Assistant Manager of Product, owning the analytics for the entire booking journey. I led a team of analysts, launched 4 products that drove millions in incremental revenue, ran A/B tests that measurably improved app conversion rates, and built the fraud detection algorithms that protected the platform.
Wanting to formalize the intersection of data science and business strategy, I earned a Master of Science in Business Analytics from the University at Buffalo, SUNY — graduating with a perfect 4.0 GPA. During my program, I also consulted for local organizations through UB's Project Clinic, turning academic knowledge into real-world impact.
Today, I'm back at LatentView Analytics as an Analytics Consultant for Product & Marketing — bringing the full loop of data engineering, product ownership, and business strategy to a Fortune 500 tech giant's Lifecycle Marketing team. I've accelerated analytics delivery by guiding a 4-person offshore team, headed the integration of AI-driven Databricks agents for automated self-serve insights, directed the implementation of a Multi-Armed Bandit testing framework that lifted engagement, and orchestrated a Tableau-to-Power BI migration with zero downtime.
I hold certifications in Microsoft Fabric, Azure Data Science, Power BI, and am a Certified ScrumMaster®. I'm also co-inventor of a patented IoT-based energy monitoring system (issued 2023).
I occasionally share insights on the tech and product world. You can subscribe to my newsletter and stay updated with the latest information.
A production-deployed LLM application demonstrating end-to-end ML engineering — from prompt design to Flask API deployment on Oracle Cloud Infrastructure. Built to explore inference optimization and real-time content generation using large language models.
An end-to-end recommender system demonstrating full-stack data engineering: ETL pipeline (Talend), cloud data warehousing (Oracle ADW), model training (Python), containerized deployment (Docker), and production hosting on Oracle Compute — mirroring enterprise ML deployment patterns.
A Retrieval-Augmented Generation (RAG) application leveraging LangChain, vector databases, and the Unstructured API to intelligently parse and query academic syllabi. Demonstrates applied NLP, document understanding, and conversational AI design.
A multi-dimensional Excel analytics dashboard for regional revenue benchmarking, store performance tracking, and product lifecycle analysis — reflecting real-world business intelligence methodologies used in retail analytics.
A Python-based web intelligence pipeline using Selenium, BeautifulSoup, Pandas, and NLTK to scrape and analyze 500+ product analyst job listings, extracting skill-demand patterns through NLP — demonstrating applied data acquisition and labor market analytics.
Advanced SQL analysis of Olympic participation data spanning decades, leveraging complex joins, window functions, and aggregate analysis to surface historical performance trends — showcasing enterprise-grade SQL proficiency.
SQL-driven analysis of credit card transaction patterns, employing aggregate functions, multi-table joins, and conditional logic to uncover consumer spending behaviors — a demonstration of financial data analytics techniques.