Hi, I'm

Vaishnavi Gaikwad

Senior Data Scientist at Intuit — turning customer data into GenAI-powered self-help experiences. From supply chains to machine learning.

VG

Supply Chain to Data Science

9+ years of turning messy data into clear decisions across manufacturing, e-commerce, and tech.

I went from optimizing physical supply chains to optimizing digital customer journeys — and I've loved every step of the transition.

Today at Intuit, I design generative AI experiences that help millions of customers find answers faster. I run experiments across products, build automated reporting pipelines, and use transcript analysis to surface insights at scale.

Before Intuit, I spent 2+ years at Wayfair where I managed analytics for 1,000+ stakeholders, reactivated supply chains to unlock $105M in revenue, and built optimization tools that improved delivery reliability by 30%. At Vista, I was the analytical lead mapping customer journeys across 5+ product teams.

I hold a Master's in Industrial Engineering from NC State University (3.75 GPA) and am a certified PMP. I'm passionate about the intersection of data science, product thinking, and customer experience.

2023 – Present

Senior Data Scientist

Intuit · Mountain View, CA

2023

Senior Analytics Engineer

Vista · Santa Clara, CA

2021 – 2023

Analytics Manager

Wayfair · Berlin, Germany

2019 – 2021

Senior Data Analyst

Wayfair · Boston, MA

2018 – 2019

Supply Chain Analyst

Wayfair · Boston, MA

2016 – 2018

M.S. Industrial Engineering

NC State University

9+
Years Experience
$105M
Revenue Unlocked
1000+
Stakeholders Managed
30%
Reliability Improvement
Python SQL Machine Learning Generative AI A/B Testing NLP LLMs Causal Inference Deep Learning Statistical Modeling Product Analytics Data Visualization Tableau Experimentation Design ETL Pipelines Data Storytelling Stakeholder Management Supply Chain Analytics

Featured Projects

End-to-end data science projects spanning recommendation systems, experimentation, GenAI, and supply chain optimization.

Collaborative Filtering SVD Python

Netflix Recommendation Engine

A hybrid recommendation system combining content-based, collaborative filtering, and popularity-based approaches. Built with real movie data, evaluating cold-start strategies and ranking metrics.

Statistics Simulation JavaScript

A/B Testing Simulator

An interactive playground for designing and simulating A/B tests. Explore power analysis, sequential testing, sample ratio mismatch detection, and multiple comparison corrections in real time.

LLM NLP Sentiment Analysis

GenAI Support Analyzer

An LLM-powered tool that analyzes customer support transcripts at scale. Classifies issues, extracts sentiment, generates summaries, and identifies trending topics using public datasets.

Optimization Forecasting Dashboard

Supply Chain Dashboard

Interactive dashboard for supply chain optimization featuring demand forecasting, route planning visualization, and KPI tracking. Bridges operational expertise with modern data science.

NLP Sentiment Analysis Trends

Twitter/X Trends & Sentiment

Interactive dashboard analyzing 2.4M tweets across 10 years. Explore trending topics, sentiment shifts, user segments, and engagement heatmaps with filterable charts.

Writing & Insights

Thoughts on experimentation, GenAI, career pivots, and lessons from the analytics trenches.

April 2026

5 A/B Testing Traps I've Seen After Running Hundreds of Experiments

"Our A/B test is significant!" is the most dangerous sentence in data science. Here's why, and what to check before you celebrate.

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April 2026

What Nobody Tells You About Using LLMs for Transcript Analysis

I used to spend 3 hours reading customer support transcripts. Now an LLM does it in 30 seconds. But getting it to work well required more data science than I expected.

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April 2026

From Supply Chain to Data Science: My Unconventional Career Pivot

5 years ago I was optimizing truck routes. Today I'm building GenAI experiences at Intuit. Here's what nobody tells you about pivoting careers.

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April 2026

I Built a Recommendation Engine From Scratch — Here's What Surprised Me

The cold start problem is real. Matrix factorization is elegant but fragile. And the best recommendations come from combining simple approaches.

Read more →

Let's Connect

Always up for a conversation about data, AI, or career pivots.

Whether you have a question, want to collaborate on a project, or just want to say hi — I'd love to hear from you.