LinkedIn | Email | GitHub | Kaggle
I am an MS in Analytics candidate at the University of Minnesota – Twin Cities, graduating in May 2026, focused on Product Data Science, Applied Machine Learning, Experimentation, and AI-powered analytics systems.
Most recently, as a Data Science & Optimization Consultant at Daikin Applied, I built predictive optimization workflows that identified $1.5M+ in annual manufacturing cost savings and reduced simulation runtime by ~90%. Previously, I worked for 2.5+ years at Tata Consultancy Services, supporting global retail data operations, reporting automation, SQL analytics, and cloud migration initiatives.
I am currently building portfolio projects around e-commerce product analytics, customer segmentation, purchase propensity modeling, A/B testing, recommender systems, and GenAI analytics copilots.
- Product Analytics: Funnel analysis, KPI design, cohort retention, customer segmentation, revenue analytics
- Experimentation: A/B testing, treatment/control analysis, hypothesis testing, bootstrap confidence intervals
- Machine Learning: Propensity modeling, imbalanced classification, recommender systems, feature engineering
- GenAI Analytics: RAG, natural-language KPI Q&A, AI executive summaries, OpenAI API, FAISS, Streamlit
- Analytics Engineering: BigQuery SQL, PySpark, Databricks, Tableau exports, data quality checks
GitHub Repo | Tableau Dashboard | AI Copilot App
Problem: E-commerce teams need to diagnose funnel drop-offs, understand customer behavior, prioritize high-value segments, predict future buyers, and evaluate campaign impact.
Solution: Built an end-to-end product analytics and experimentation platform using BigQuery SQL, Python, Tableau, Scikit-learn, Streamlit, OpenAI API, FAISS, and SentenceTransformers.
Highlights:
- Analyzed 4.3M+ GA4 e-commerce events across 360K sessions and 270K users
- Built Tableau dashboards for executive KPIs, funnel analysis, category performance, cohort retention, and customer segmentation
- Developed a leakage-safe purchase propensity model with 0.847 ROC-AUC and 15.8x PR-AUC lift
- Evaluated randomized email campaign lift using z-tests and bootstrap confidence intervals; Mens E-Mail drove +$0.77 revenue/user lift
- Added an AI analytics copilot for natural-language KPI Q&A, metric documentation RAG, executive summaries, and experiment interpretation
Skills: Product Analytics, BigQuery, SQL, Python, Tableau, Scikit-learn, A/B Testing, RAG, OpenAI API, Streamlit, FAISS
Problem: Online grocery platforms need to predict repeat purchases, personalize baskets, and identify cross-sell opportunities across millions of customer-product interactions.
Solution: Built a PySpark + Databricks pipeline on 30M+ Instacart order-product records, engineering leakage-safe user, product, user-product, recency, and reorder-behavior features.
Impact:
- Trained Spark ML models for reorder prediction with 0.409 PR-AUC
- Achieved ~4x lift over baseline and 58% Recall@10
- Added FP-Growth market basket rules and customer segmentation for personalized retail strategies
Skills: PySpark, Databricks, Spark ML, Feature Engineering, Recommender Systems, Customer Segmentation
Problem: Market data is noisy, volatile, and difficult to operationalize without repeatable forecasting and validation workflows.
Solution: Built an automated Python forecasting pipeline on 26+ years of global market index data, engineering lag, rolling-window, volatility, and momentum features.
Impact:
- Compared tree-based, LSTM, and statistical baseline models
- Designed repeatable validation workflows using Python, TensorFlow, AWS, and GitHub Actions
- Structured the project as a reproducible forecasting and market-signal analysis pipeline
Skills: Python, Time Series, TensorFlow, Scikit-learn, AWS, GitHub Actions
I am currently focused on roles in:
- Product Data Science
- Applied Machine Learning
- Experimentation / Growth Analytics
- Customer Analytics
- Retail / E-Commerce Analytics
- AI-powered analytics and decision-support systems