Healthcare Data Engineer

TENNYSON YESUPATHAM

Data Engineering & AI/ML Professional

Boston, Massachusetts 603-***-**** ***********@*******.*** linkedin.com/in/tennyson-y-a543b815 PROFESSIONAL SUMMARY

Senior technical leader with 20+ years of experience specializing in enterprise data architecture, AI/ML systems, and cloud technologies. Proven expertise in healthcare data security, ETL pipelines, and large-scale distributed systems. CORE TECHNICAL SKILLS

AI/ML: Deep Learning (CNN, LSTM, U-NET), Object Detection (YOLO, R-CNN), Image Segmentation, Autoencoders Computer Vision: Image Processing, Video Analysis, Object Tracking, Semi-supervised Learning ML Frameworks: TensorFlow, PyTorch, Keras, scikit-learn Data Engineering: ETL Pipelines, Snowflake, HIPAA Compliance, Data Lakes Cloud & Infrastructure: AWS, Azure, Hadoop, Spark, Docker, Kubernetes Languages & Databases: Python, Java, SQL, Oracle, PostgreSQL, MongoDB FEATURED PROJECTS

Manufacturing Defect Detection System Caltech July 2024--Present Technologies: YOLOv5, U-NET, TensorFlow, Python, Computer Vision Developed real-time defect detection system achieving 95% accuracy Implemented custom CNN and U-NET architecture for image segmentation Built automated alerting system for quality control monitoring Deployed model using TensorFlow Serving for real-time inference Tourism Recommendation Engine Caltech July 2024--Present Technologies: TensorFlow, Python, Collaborative Filtering Developed personalized recommendation system achieving 88% accuracy Implemented hybrid approach combining collaborative and content-based filtering Built scalable architecture processing historical tourism data Financial Risk Assessment Platform Caltech July 2024--Present Technologies: TensorFlow, scikit-learn, Python

Built neural network for loan default prediction with 89% accuracy Implemented SMOTE for handling imbalanced datasets Created automated risk scoring system processing 100K+ applications Predictive HR Analytics System Caltech July 2024--Present Technologies: scikit-learn, Python, Random Forest

Developed employee turnover prediction model with 87% accuracy Implemented K-means clustering for employee risk segmentation Built interactive dashboard for real-time risk monitoring PROFESSIONAL EXPERIENCE

Lead Member of Technical Staff (LMTS)

Athenahealth May 2018--September 2024

Data Engineering & Enterprise Architecture

Architected enterprise-wide data lake using Snowflake, processing petabyte-scale healthcare data Led HIPAA-compliant PHI protection implementation using Delphix Managed multi-node database infrastructure across Oracle and PostgreSQL Revenue Forecasting & Analytics

Developed LSTM-based revenue forecasting model achieving 92% accuracy Implemented time series analysis for multi-state healthcare revenue prediction Created automated reporting system for revenue forecasting using Python and Tableau Integrated forecasting model with financial planning systems HR Analytics & Workforce Intelligence

Developed employee attrition prediction model using Random Forest (85% accuracy) Created HR data mart integrating multiple data sources for workforce analytics Built automated reporting dashboard for executive leadership Marketing Analytics & Lead Scoring

Engineered ML-based lead scoring system using gradient boosting algorithms Achieved 78% accuracy in predicting high-value customer conversions Integrated predictive model with CRM system using REST APIs Implemented A/B testing framework for marketing campaign optimization Senior Big Data Engineer

Gateway Health Oct 2017--Feb 2018

Built distributed data processing pipelines using Hadoop MapReduce Developed document management system with OCR capabilities Senior Big Data Engineer

Qlik Podium Data Sep 2016--Jul 2017

Designed data ingestion framework using Java and REST APIs Implemented multi-node Hadoop clusters

EDUCATION

Caltech AI & Machine Learning Bootcamp (2024)

Caltech Post Graduate Program in Cloud Computing (In Progress, 2025) Bachelor of Engineering, Vellore Institute of Technology (1991-1995)

Contact this candidate