Open to opportunities

Shreyas Mysore Narayana

@shreyasmysorenarayan

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Data scientist specializing in predictive modeling, ML, and scalable data solutions for healthcare and engineering.

United States

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What I'm looking for

I'm seeking a mid-level data scientist role building production-ready ML models and pipelines in healthcare or engineering, with collaborative teams, cloud deployment responsibilities, and opportunities for applied research and measurable business impact.

I am a data scientist specializing in predictive modeling, machine learning, and statistical analysis, with experience applying ML to healthcare, engineering, and insurance problems.

At the University of Houston I built predictive models on 500,000+ MRI and brain-scan data points achieving up to 88% accuracy for DBS outcome prediction and applied LASSO, Recursive Feature Elimination, and genetic algorithms to reduce features by 30% while improving generalizability.

In industry I developed SQL-based ETL pipelines for 10M+ records, created Tableau dashboards that halved reporting time, and delivered end-to-end solutions including synthetic augmentation with diffusion models and an Edge AI smart-parking system that achieved 93% accuracy.

I hold a Master of Engineering in Data Science (3.96/4.0) and certifications in generative AI and cloud computing. I seek roles where I can deliver scalable ML solutions, mentor teammates, and translate data into measurable business impact.

Experience

Work history, roles, and key accomplishments

Graduate Assistant, Facilities Services

University of Houston

Jan 2025 - May 2025 (4 months)

Managed work order systems and Cougar Card access while building a relational database to map physical keys to authorized personnel, improving data integrity and access control and optimizing cart inventory scheduling to reduce delays.

Relational Databases SQL Access Control Data Modeling Inventory Management Process Optimization

Research Assistant

University of Houston

Jan 2024 - Jan 2025 (1 year)

Built predictive models (SVM, Logistic Regression, Random Forest) on 500,000+ MRI and brain-scan data points achieving up to 88% accuracy for predicting DBS outcomes; applied LASSO, RFE, and genetic algorithms to reduce features by 30% and used diffusion-model augmentation to increase dataset diversity by 20%.

Python PyTorch scikit learn SVM Diffusion Models A B Testing Statistical Analysis

Junior Data Analyst

Konigtronics (OPC) Private Limited

May 2022 - May 2023 (1 year)

Developed and optimized SQL-based ETL pipelines for 10M+ records to improve reporting speed and reliability; built Tableau dashboards that reduced manual reporting time by 50% and performed regression analysis and statistical tests to improve campaign ROI.

SQL ETL Tableau Data Warehousing Performance Optimization