Data Scientist at Verizon · Open to opportunities

Astrophysicist. Data Scientist. Storyteller.

I used to study black holes. Now I find them in datasets — both are equally mysterious and require a concerning amount of coffee.

Machine LearningXAI FrameworksCausal InferencePython & SQLGeorgia Tech MS
See my work ↓ Get in touch
$1.2B+
Bad-debt impact
73%
Faster hypotheses
3+ yrs
At Verizon
2×
STEM degrees
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About

The person
behind the models

I'm Geet — a Data Scientist at Verizon who started out studying black holes at Rutgers and somehow ended up finding anomalies in billion-dollar datasets instead. Turns out the universe is full of patterns whether you're looking at galaxies or customer churn.

I'm currently a few semesters into my Georgia Tech Master's in Computational Analytics — hanging in there by a thread token, but still going.

Outside of work: unhealthy amounts of anime, losing LP in League of Legends, and occasionally remembering to touch grass.

PythonSQLTensorFlowGCP / BigQueryStreamlitDataRobotApache SparkDockerTableauJava
🌌

Dual B.S. — Astrophysics & CS

Rutgers University, 3.7 GPA. Society of Physics member, minor in Mathematics. The kind of person who thinks statistical mechanics was just ML before it was cool.

📡

Hosted the Verizon Analytics Summit

Presented at the 2023 summit alongside executives from Verizon, Bayer, BNY Mellon, NY Life, and ThoughtSpot. No pressure.

🧬

Trained ML on mice

At the Human Genetics Institute of NJ, built DeepLabCut models for kinematic pose estimation on wet-lab video. Yes, really.

🎓

Georgia Tech MS — Computational Analytics

“Check back in 3 years to see if I get a 4.0.” — my actual stance on the degree.

Experience

Where I've been,
what I've broken built

3+ years turning data into decisions at scale. Every number below is real.

Verizon
Nov 2024 — Present

Data Scientist III · Engineer III

  • Architected a credit-risk fullstack app — synthetically generating a customer database to monitor policy changes impacting $1.2B+ in bad-debt writeoff.
  • Engineered 9 Python & SQL anomaly-detection microservices with Verizon EDW integration, saving 50+ hours/month across 15 teams.
  • Led 2 ML XAI frameworks (Streamlit + Qlik), reducing hypothesis-generation time by 73% for shareholder calls.
  • Built churn-forecasting models and intervention simulators (SMS, email, fee adjustments) — increased decisioning coverage from 0% → 60%.
  • Presented to 100+ stakeholders, directly influencing product roadmap and reducing future development costs.
↑ $1.2B impact · 50h/mo saved · 73% faster
Verizon
Jan 2023 — Nov 2024

Data Scientist I

  • Established a centralized DS pipeline framework/API for Corporate Finance via GitLab + Domino + GCP — 63% more efficient collaboration.
  • Built a real-time AT&T and T-Mobile port-in EDA pipeline using DataRobot to forecast competitor market-share trajectories.
  • Automated serverless forecasting for 9 major value brands (Walmart, Tracfone, Straight Talk, FiOS), pioneering a 12h email-report system.
  • Developed Tableau dashboards on GCP/BigQuery, eliminating 10+ hours of weekly manual reporting.
  • K-Means customer segmentation — identified 3 at-risk cohorts that directly informed new retention campaigns.
↑ 63% efficiency gain · 10h/wk eliminated
Verizon
Jun 2022 — Aug 2022

Predictive & Prescriptive Analytics Intern

  • First exposure to Verizon's data infrastructure — the internship that turned into 3+ years.
Human Genetics Institute of NJ
Oct 2021 — Jun 2022

Data Science Intern

  • Trained DeepLabCut ML models for kinematic pose estimation on wet-lab mouse video — Python, TensorFlow, NVIDIA CUDA.
  • Statistical validation via ANOVA and T-tests, benchmarked against published findings (Jones et al. 2020).
  • Sponsored 4 Oxford iMARS Python extensions for retinal-cord tissue post-microscopy analysis.
↑ Neuroscience × ML
RankSense
Jan 2021 — Oct 2021

Data Science Intern

  • Researched SEO optimization across 100+ client domains — A/B testing via Bayesian methods and CausalImpact.
  • Improved click-through and baseline-conversion rates by ~32%.
↑ 32% CTR improvement
Projects & Case Studies

Things I've built
that actually worked

A mix of work projects (sanitized), academic deep cuts, and personal experiments.

01 · Featured

Credit Risk Intelligence Platform

Fullstack app that synthetically generates customer databases to monitor credit-policy changes and their downstream impact on bad-debt writeoff. Built end-to-end — data pipeline, ML models, and Streamlit frontend — so finance teams can simulate policy scenarios without ever touching production data.

PythonStreamlitGCPSQLSynthetic Data
↑ $1.2B+ in bad debt monitored
02

XAI Gross-Adds Framework

Explainable-AI framework isolating the real-time drivers of Verizon Gross Adds. Cut hypothesis-generation time by 73% for shareholder calls — no more “we think it might be…” moments.

XAIQlikPythonML
↑ 73% faster decision-making
03

Gravitational Microlensing Sim

Academic project simulating gravitational microlensing events — the same physics that bends light around black holes. Built in Python with Jupyter. The astrophysics degree, paying dividends.

PythonAstrophysicsNumPy
↑ github.com/geetpurohit
04

Churn Prediction & Intervention Engine

ML models forecasting customer churn and simulating responses to interventions (SMS, email, fee adjustments). Pushed internal decisioning coverage from 0% → 60%, cutting outsourced engagement volume by ~35%.

MLPythonSimulationDataRobot
↑ 0% → 60% coverage
05

DeepLabCut Pose Estimation

Trained computer-vision models to track mouse joint positions in wet-lab video experiments. Kinematic data benchmarked against published neuroscience research. The weirdest and most interesting project I've worked on.

TensorFlowCUDAComputer VisionR
↑ Neuroscience × Deep Learning
06

AniParty — Video Sync Extension

Personal project: a browser extension that syncs video playback between computers. Built in JavaScript, because watching anime with friends across the country shouldn't require a scheduling meeting.

JavaScriptBrowser ExtensionWebSockets
↑ Solving the important problems
Skills

The toolkit

Proficiency built by shipping things, not just finishing courses.

Languages

Python 96
SQL 92
Java 78
JavaScript 70
R 72

ML / Data

Pandas / NumPy 95
Scikit-learn 90
DataRobot 88
TensorFlow 85
Apache Spark 75

Infrastructure

GCP / BigQuery 88
PostgreSQL 85
GitLab CI/CD 82
Docker 78
MongoDB 70

Visualization

Tableau 90
Streamlit 88
QlikSense 85
Plotly 82
D-Tale 80
Contact

Let's talk.
Seriously.

Whether it's a job opportunity, a collaboration, or you just want to argue about whether anime is peak storytelling — my inbox is open.

LinkedIn GitHub Resume
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