Surya Vakkalagadda
Data Scientist @GetInsured
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WORK HISTORY
Data Scientist @GetInsured
Mountain View, CA, US
Built DataSync, a near real-time platform integrating NICE APIs with internal databases to process large volumes of call and chat interactions using OpenAI-powered NLP, with quality validated through intent accuracy and sentiment consistency.Validated and evaluated supervised fraud detection models on enrollment data using Python, including feature engineering and precision-recall analysis, contributing to reducing improper insurance subsidy payouts.Led complex SQL analyses on prior-year enrollment data to identify Open Enrollment trends and opportunities, informing state-level targeting and growth strategies.Developed and delivered Sisense dashboards to monitor Open Enrollment performance and support data-driven decision-making.Optimized PySpark ETL pipelines processing high-volume daily data, improving analytics and reporting efficiency.Supported the production lifecycle of machine learning models by validating outputs, monitoring performance metrics, and collaborating with engineering teams on model improvements.
EDUCATION
San José State University
Master's degree, Artificial Intelligence
Madanapalli Institute of Technology & Science, Madanapalli
Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering
ABOUT SURYA VAKKALAGADDA
I am a Data Scientist with experience in applying machine learning, statistical analysis, and LLM-powered NLP systems to large-scale healthcare and user interaction data. Currently at GetInsured, I work on building data-driven solutions that translate complex datasets into actionable insights and measurable business impact. My work includes developing near real-time data platforms integrating APIs and internal systems, applying NLP techniques to analyze call and chat interactions, and supporting fraud detection models through feature engineering and model evaluation. I also perform advanced SQL analysis to uncover trends in enrollment data and build dashboards that enable data-driven decision-making at scale. Additionally, I optimize ETL pipelines and contribute to the end-to-end lifecycle of machine learning models in production environments.I have a strong foundation in Python, SQL, PySpark, and data engineering, along with experience in A/B testing, anomaly detection, and large-scale data processing. My academic work includes evaluating LLMs and mentoring students in advanced data mining.I am particularly interested in leveraging AI and data science to solve real-world problems, especially in domains involving large-scale systems, user behavior, and intelligent automation. I am always open to connecting with professionals working on impactful and innovative data-driven solutions.
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