Manoj Parmar
Senior Data Scientist
New York, New York, United States
Summary
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Senior🎓
Top SchoolManoj Parmar is a Senior Data Scientist based in New York with a decade of experience applying ML and analytics across FinTech, insurance, consumer goods, and research settings. He has a proven track record delivering high-impact models in production—driving an estimated $9M annual benefit on credit products at AB InBev and spearheading initiatives at Prudential projected to save ~$7M by reducing claim rates. Comfortable with end-to-end pipelines, he builds gradient-boosted and deep learning models (LGBM, XGBoost, CNNs) in cloud environments like Azure Databricks and applies creative data transformations to unlock novel signals. He also mentors and teaches at scale, having supported 450+ online MS Analytics students as a graduate teaching assistant at Georgia Tech. Equally at home in algorithmic trading research and consumer-sensory analytics, Manoj blends rigorous experimentation (A/B testing, backtesting) with business-facing deployment and stakeholder communication. His cross-domain curiosity—from gait analysis visualizations to transforming time series into 2D inputs for CNNs—drives practical innovation beyond conventional feature engineering.
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS Analytics, Master of Science - MS Analytics at Georgia Institute of TechnologyIndian Institute of Technology BombaySecondary School Certificate (SSC), Secondary School Certificate (SSC) at Bombay Cambridge SchoolHigher Secondary Certificate (HSC), Higher Secondary Certificate (HSC) at Bhavans College
English, Hindi
Github Skills (12)
predict6
stock-price5
machine-learning4
deep-learning4
stock4
sentiment-analysis4
nlp3
data-science3
natural-language-processing3
nlu1
python1
text-based1
Programming languages (1)
Github contributions (5)
Manoj98/Deep-Learning-Project
Nov 2023 - May 2024
Code Project Files for CSE 8803 - DLT Deep Learning for Text
Contributions:3 pushes in 5 months
Manoj98/Improving-Question-Answering-with-external-context-fusion
Sep 2023 - Nov 2023
Question answering system based on QA-GNN architecture with utilization of external context. QA-GNN involves using language models(LMs) to encode QA context and retrieve a knowledge subgraph (KG) from the QA entities. The joint representation of QA context and KG sub graph is passed through graph neural network(GNN) module for reasoning.
Contributions:1 push in 2 months
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