Paras Varshney is a Senior Data Scientist and ML engineer based in Boston with 8 years of experience building production-grade models and MLOps pipelines across finance, academia, and industry. Currently at Fidelity he designs fraud-detection systems that fuse internal behavior data with external threat intelligence to protect diverse business lines, while optimizing models for resource-constrained deployments. His background includes high-impact research at Northeastern and collaborations with MIT on underwater acoustic analytics (95% accuracy, real-time Streamlit tooling for 200M samples) and operational ML for smart cities and transport forecasting. A Kaggle Master and former Global Data Science Ambassador for Z by HP, he blends hands-on model engineering with public speaking and mentorship at NVIDIA GTC and ODSC, and has a track record of shrinking inference and pipeline costs through pragmatic retraining and feature engineering. Notably, he has translated research prototypes into funded programs and productized solutions (e.g., 71% invoice processing time reduction), demonstrating an unusual ability to move from signal-level research to enterprise impact.
8 years of coding experience
5 years of employment as a software developer
Bachelor's Computer Engineering , Bachelor's Computer Engineering at Indian Institute of Information Technology Design & Manufacturing, Kurnool
Master's degree Data Analytics Engineering, Master's degree Data Analytics Engineering at Northeastern University
This is a personal portfolio website that serves as a platform to showcase my skills, experiences and achievements. The website is still under development and I welcome contributions from the community. The website features an about section that provides an overview of who I am and what I do, a portfolio section that displays my work.
Contributions:87 commits, 12 PRs, 100 pushes in 2 years 4 months
Contributions:16 commits, 13 pushes, 1 branch in 10 months
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