Data science that ships, in whatever domain the problem lives.
Product-Focused Data Scientist Turning Models into Decisions
I build models that make high-stakes decisions in production. At Tesla, every 4680 battery cell passes through my computer vision models as the final quality gate before it goes into a vehicle. Before that, I worked on customer analytics in retail, credit and fraud risk in banking, and engine maintenance in aerospace.
Experience
Five roles, one thread: turning data into decisions people act on.
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Jul 2024 – Present
Senior Data Scientist · Tesla
Own quality and risk decisions for Tesla's battery cells: the final quality gate for every 4680 cell, thermal-runaway and warranty-life forecasting, and the dashboards engineers use daily.
10+ cell programs qualified98% recall45 engineers, 4 teams200+ hrs/week saved -
Sep 2023 – Jun 2024
Data Scientist · Standard AI
Ran in-store experiments and, with product and sales, defined the shopper-engagement metrics behind a retail analytics platform.
+29% conversion$600K new contracts+15% revenue -
Jan 2023 – Sep 2023
Cell Engineer Intern · Tesla
CT-based defect detection and quality dashboards that drove battery cell design changes.
+40% defect recall−20% electrolyte leaks -
Jun 2022 – Aug 2022
Summer Associate, Credit Fraud Risk · JPMorgan Chase
Small-business loan default modeling, with risk segments explained for regulators.
+14% model lift -
Oct 2019 – May 2020
Undergraduate Researcher · Rolls-Royce
Engine-maintenance pipeline and R Shiny tool for flagging at-risk aircraft.
3–4 hrs → under 5 min
Featured work
Public write-ups of work I've contributed to, plus my patent and papers.
Engineering Tesla Vehicle Batteries for Safety, Durability and Longevity
How Tesla inspects every 4680 cell, using X-ray, CT scanning and AI-based defect detection, and how fleet telemetry feeds back into battery safety. This is the area my models and risk forecasting work in.
Read on tesla.com → Retail · Standard AIHeatmaps Are Failing Retailers. What's Next?
Why camera-by-camera heatmaps mislead retailers, and how 3D spatial reconstruction of real shopper paths gives a truer picture of in-store behavior.
Read on standard.ai → Patent · Standard AISubject-tracking in a cashier-less store for spatial analytics
US 2024/0193497 A1. Co-inventor on using subject tracking in autonomous-checkout stores to improve item and shelf placement.
View patent → Paper · Standard AIAnalyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store
An algorithm that detects when shoppers browse a shelf from 3D-tracked trajectories, calibrated and tested across two stores. Second author (Morales, Vijay, Zanlungo, Woollard).
Read paper → Paper · UCSDEmpirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models
Compares BitFit and adapter modules with full fine-tuning on GLUE tasks. BitFit matches full fine-tuning with far fewer trainable parameters and stays stable with just 30% of the data. Co-author.
Read paper → Research · CMSAC 2021An Interpretable Method of Learning Stochastic Game Dynamics
Models a soccer ball's movement with learned potential functions to simulate matches. Captures large score differentials better than Poisson regression. Presented at the Carnegie Mellon Sports Analytics Conference.
View slides → Project · UCLA DataResMake-A-Monet: Image Style Transfer with CycleGANs
Turns everyday photos into Monet-style paintings with CycleGAN and Pix2Pix, served through an interactive Plotly Dash app.
Read on Medium →Awards
- Tesla Spot Option GrantStock options for outstanding contributions to the company, awarded as the top performer on my team · Oct 2025
- 1st Place, Chevron TrackRice University Datathon · 2021
- 2nd Place, Prosperity TrackUCSD DataHacks · 2021
- 1st Place OverallDSS Datathon for Social Good, UC Berkeley · 2020
- 3rd Place, City Search TrackTAMU Datathon · 2020
- 2nd Place OverallRetina AI R Datathon · 2020
- Gandhi Memorial ScholarshipSan Diego Indian American Society · 2019
Domains
Different industries, the same core work: turn messy, high-volume data into a decision someone can trust.
Lithium-ion battery reliability
Lab stress testing, CT-scan computer vision, defect detection, and thermal-runaway risk forecasting from fleet telemetry.
Shopper behavior analytics
3D pose data, in-store A/B testing, and layout simulation for physical retail.
Credit & fraud risk
Loan default modeling at scale, with explanations built for regulatory audits.
Aerospace maintenance
Engine health algorithms and geospatial tooling for fleet maintenance.
Toolkit
Languages & ML
AI & Data Platforms
Analytics
Education
Let's talk
Based in the San Francisco Bay Area. I'm happy to hear about data science roles and interesting problems in any domain.