Vatsal Shah
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Applied AI Lead @ Intuit Credit Karma
7+ years of experience building 0→1 AI products at Credit Karma, scaled to 120M+ members delivering double-digit improvements in revenue and engagement over my tenure. I've owned roadmaps, influenced cross-functional strategy, and driven alignment across research, product, and engineering. I enjoy taking ambiguous, high-stakes AI problems from research to production. Feel free to reach out to me at: shah.vatsalm@gmail.com Experience: - Founding scientist on Credit Karma's generative AI financial assistant. Led the full stack -> fine-tuning, RAG, responsible AI, latency optimization, and evaluation (from zero to production) - Also led home feed ranking and personalization for 120M+ members, and owned approval odds models across Credit Karma's core financial products. Technical Expertise Generative AI: LLM Fine-tuning, RAG, Data Curation, Responsible AI, Latency Optimization, and Evaluation Metrics. Machine Learning: Ranking & Personalization, Recommender Systems, Deep Learning, Ad Ranking, and Financial Risk Modeling. Engineering: Python, SQL, TensorFlow, PyTorch, Kubernetes, Spark, Airflow, Kafka, Google Dataflow, and BigQuery. Analytics: Statistical Analysis, A/B Testing, Causal Inference Education: MS in Computer Science (Artificial Intelligence focus) from the University of Southern California.
University of Southern California
Master’s Degree, Computer Science (Data Science)
January 1, 2016 – January 1, 2018
University of Mumbai
Bachelor's Degree, Information Technology
January 1, 2012 – January 1, 2016
Lilavatibai Podar Senior Secondary School
High School, Physical Sciences
January 1, 2010 – January 1, 2012
Credit Karma
Lead AI Scientist
June 1, 2018 – Present
San Francisco Bay Area · Hybrid
Fair.com
Data Scientist Intern
May 1, 2017 – August 1, 2017
Greater Los Angeles Area
University of Southern California - Marshall School of Business
Graduate Student Researcher (Machine Learning)
January 1, 2017 – May 1, 2018
Greater Los Angeles Area
ActoFit Wearables
Machine Learning Intern
December 1, 2015 – February 1, 2016
Mumbai Area, India
GoAnalytics
Data Analyst Intern
July 1, 2015 – August 1, 2015
Noida Area, India
Pristine InfoSolutions
Summer Trainee (Information Security)
July 1, 2014 – July 1, 2014
Mumbai Area, India
Microsoft
Student Associate
January 1, 2014 – January 1, 2015
Mumbai Area, India
USC Graduate Hackathon - Face Clustering
March 1, 2017 – Present
Given a set of face images , partition this set into non-overlapping clusters (subsets) such that: Homogeneity Criterion: Each cluster only contains face images belonging to the same subject Completeness Criterion: All face images belonging to a subject are partitioned into the same subset.
Santander Bank Customer Satisafaction Problem on Kaggle Rank: (338/5236)
March 1, 2016 – May 1, 2016
Santander Bank is asking Kagglers to help them identify dissatisfied customers early in their relationship. Doing so would allow Santander to take proactive steps to improve a customer's happiness before it's too late. In this competition, I worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.
Date Your Data Hackathon on AnalyticsVidhya (Sponsored by Internshala)(Stood among top 10% from over 2000 participants)
February 1, 2016 – Present
Developed an intelligent matching algorithm could help Internshala users get better experience and enhance chances of meaningful profile matches.
Sentimental Analysis using data from social networks
June 1, 2015 – Present
https://crocodile.shinyapps.io/SentimentalAnalysis/ Working on improving current algorithms to improve the accuracy of sentiment classification using principles of Machine Learning, Natural Language Processing and Data Mining. Used python for development of machine learning algorithms on the back end. Achieved classification accuracy of 83.45 % using a bagging classifier designed by me. Used R and RShiny for front end data visualization.
Predictive model for Car Sales dataset using Naive Bayesian Classification.
March 1, 2015 – Present
Developed a predictive model and prepared a business intelligence report by studying a dataset for car sales and interpreted the results for the same. Also, advised the business decision to be taken based on the data.
Broadcast Messenger Application
February 1, 2015 – Present
Developed an interactive chat application to broadcast messages over a network. Implemented concepts of socket programming using JAVA.
Student Location Tracker Using Google Maps API
January 1, 2015 – Present
Web based application to track student location using the Google Maps API. Developed the application using JavaScript, JQuery, HTML, CSS and JSON.
Python
TestDome
June 24, 2026 – Present
Custom Models, Layers, and Loss Functions with TensorFlow
Coursera
June 24, 2026 – Present
Accelerating Innovation with AB Testing
Maven
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, including hackathons and personal projects, indicates a strong passion for data science and continuous learning. Their experience in various industries (finance, automotive, wearables) and academic research demonstrates adaptability. The 'Lead AI Scientist' role at Credit Karma, involving generative AI and personalization, aligns with a culture of innovation and impact. However, the target role is 'Data Analyst', which might be a step down from their 'Lead AI Scientist' experience, potentially impacting long-term cultural fit if growth opportunities are not clearly defined.
Soft Skills & Operational Fit
The candidate's experience as a 'Lead AI Scientist' and 'Graduate Student Researcher' suggests strong problem-solving, leadership, and research skills. Collaboration across engineering and business units is also evident, indicating good teamwork and communication for operational fit. The hackathon participation and project diversity show initiative and a proactive approach to learning and applying skills.