---
title: "Urvansh Shah - Research Assistant in United States | Tal"
description: "Meet Urvansh Shah, Research Assistant @ Arizona State University | Master’s in Data Science | Ex-Data Science Intern @ Sila Nanotechnologies | Ex-Analyst @ Merkle | 3x National Hackathon Winner | LinkedIn Top Voice ‘24 | Microsoft based in United States. AI stack includes antigravity, codex, and cl..."
image: "https://tal.club/meet/urvansh-shah/share-image"
---

# Urvansh Shah on Tal

## Headline

Research Assistant @ Arizona State University \| Master’s in Data Science \| Ex\-Data Science Intern @ Sila Nanotechnologies \| Ex\-Analyst @ Merkle \| 3x National Hackathon Winner \| LinkedIn Top Voice ‘24 \| Microsoft

## About

I build scalable data systems and machine learning solutions that turn complex data into real\-world impact\. Currently pursuing a Master’s in Data Science at Arizona State University \(GPA: 3\.9/4\.0\), with hands\-on experience as a Research Assistant working on applied machine learning and system\-driven problems at the intersection of data, computation, and real\-world applications\. Most recently, I worked as a Data Science Intern at Sila Nanotechnologies, where I engineered production\-grade ETL and simulation pipelines processing 500K\+ sensor and experimental records weekly using Python, Snowflake, and Airflow\. I also built full\-stack systems for physics\-based simulations using FastAPI and PyBaMM, enabling real\-time experimentation and supporting multiple R&D workflows\. Prior to this, I spent over two years at Merkle as a Data Analyst and software\-focused data engineer, developing large\-scale ML and NLP pipelines on AWS\. I improved prediction accuracy by 55% and reduced processing time from 6 hours to 34 seconds, with automation and forecasting initiatives contributing to a 500% increase in revenue\. Earlier in my career, I interned at Microsoft as a Software Engineer, where I built cloud\-based services and REST APIs using Azure, strengthening my foundation in backend engineering and distributed systems\. I bring 4\+ years of experience across data science, software engineering, data engineering, and applied machine learning, working end\-to\-end on designing, deploying, and optimizing scalable cloud and data systems\. My work spans building robust ETL pipelines with Airflow and Spark, developing machine learning models for NLP and predictive analytics, deploying MLOps systems using MLflow, Docker, Kubernetes, and CI/CD pipelines, and creating full\-stack, cloud\-based applications using FastAPI and real\-time analytics architectures\. I also contribute to applied research at Arizona State University, working on interdisciplinary projects that combine data science, physics, and hardware systems\. I have been recognized as a LinkedIn Top Voice ‘24 and a 3x National Winner, with leadership experience as Head Boy \(NSS Mumbai\), reflecting strong ownership, collaboration, and initiative\. I am particularly interested in solving problems at the intersection of data infrastructure, machine learning, and scalable systems, especially in high\-impact and research\-driven environments\. Open to full\-time opportunities for 2026 in Data Science, Machine Learning Engineering, and Software Engineering\. 📧 urvanshshah9@gmail\.com

## Location

United States

## AI stack

antigravity

codex

claudecode

## Work experience

Research Assistant at Arizona State University, Nov 2025 \- Present · 10 mos, Tempe, AZ

Data Science Intern at Sila Nanotechnologies, Inc\., Jun 2025 \- Nov 2025 · 6 mos, California, United States, • Engineered and optimized ETL pipelines processing 500K\+ sensor and simulation records weekly using Snowflake, Python, and Airflow, accelerating lithium\-ion cell research throughput by 35% and reducing manual processing time by 60%\. • Programmed a modular full\-stack GUI for PyBaMM simulations via FastAPI, SQLite, and JavaScript, enabling interactive configuration of physics\-based models and real\-time visualization, supporting 10\+ simulations across 3\+ R&D teams\. • Applied machine learning models \(regression, classification\) via Scikit\-learn and Pandas to predict battery material performance and degradation patterns, achieving 15% higher model precision through feature selection and optimization\. • Automated data ingestion and transformation workflows with Airflow DAGs for battery simulation and experimental datasets, ensuring reliable data delivery pipelines and shortening experimentation cycles by 25% for digital twin model development\. • Collaborated with senior scientists to design data\-driven experiments for lithium\-ion battery optimization, integrating simulation outputs into performance dashboards and supporting 5\+ workflows for electrode configuration research\.

Graduate Services Assistant \- Teaching Assistant at Arizona State University, Aug 2024 \- May 2025 · 10 mos, Tempe, Arizona, United States, Graduate Services Assistant \- Teaching Assistant and Lead Lab Grader at Arizona State University\.

Data Analyst at Merkle, Jul 2023 \- Jun 2024 · 1 yr, Chicago, IL, • Devised and deployed scalable AI/ML models via TensorFlow, PyTorch, and Scikit\-learn, optimizing NLP workflows for large\-scale data processing on AWS \(Lambda, S3, Redshift, SageMaker\), improving prediction accuracy by 55%\. • Operationalized Python automation with AI\-driven forecasting models, reducing time from 6 hours to 34 seconds, elevating operations with checklists, and collaborating with clients to refine functionality, driving a 500% revenue increase\. • Fabricated large\-scale ETL and real\-time analytics pipelines using Apache Spark, SQL, and Airflow, automating feature engineering and model training workflows, increasing data ingestion speed by 40%, accelerating business decisions\. • Designed and implemented an end\-to\-end CI/CD MLOps pipeline via MLflow, Docker, Airflow, and Kubernetes, enabling automated model versioning, cloud\-based deployment, and monitoring, reducing deployment time by 45%\. • Created interactive dashboards and balanced reporting systems using Power BI, Tableau, and SQL, integrating real\-time KPIs and predictive analytics to drive data\-driven decision\-making, reducing manual reporting efforts by 50%\.

Associate Analyst at Merkle, Aug 2022 \- Jul 2023 · 1 yr, Chicago, Illinois, United States

Associate Software Engineer at Merkle, Jul 2022 \- Aug 2022 · 2 mos, Chicago, Illinois, United States

Technical Engineer at Mahaveer Technologies, Jan 2022 \- May 2022 · 5 mos, California, United States, • Architected a cloud data warehouse on Snowflake & Redshift, automating ETL with Spark elevating performance by 50%\. • Formulated and pioneered data governance framework using dbt & Great Expectations, guaranteeing 99\.8% data accuracy\. • Integrated a fraud detection system via Neo4j & XGBoost, reducing 42% financial risks and fraudulent cases\. • Constructed A/B testing pipelines with Bayesian optimization, refining marketing strategies and increasing CTR by 23%\. • Orchestrated real\-time streaming analytics dashboards with Kafka & BigQuery, boosted data visibility by 57%\.

## Education

Master of Science \- MS, Data Science, Arizona State University

Bachelor of Engineering \- BE, Electronics and Telecommunication Engineering, St\. Francis Institute Of Technology

[View the original profile](https://tal.club/meet/urvansh-shah)

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