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Mihir Pandya

@mihirpandya

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Aspiring data scientist with a strong background in machine learning.

United States

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What I'm looking for

I am looking for a role that fosters innovation and collaboration, where I can apply my data science skills to solve complex problems and contribute to meaningful projects.

I am a dedicated and passionate data science professional currently pursuing my Master's degree at The University of Texas, where I maintain a perfect GPA of 4.0. My academic journey has equipped me with a solid foundation in data analysis, machine learning, and project management. I have successfully applied my knowledge in real-world scenarios through internships, where I designed and deployed innovative solutions that significantly improved processing times and user experiences.

During my internship at Hexa Planet Solution, I developed a .NET and SQL backend that integrated machine learning logic, resulting in a 20% reduction in processing time for client queries. Additionally, I automated data ingestion processes, saving over two hours of manual work daily. My experience as a Machine Learning Intern at InternshipStudio allowed me to build predictive models that enhanced decision-making for over 1,000 users. I am driven by the challenge of transforming complex data into actionable insights and am eager to contribute to impactful projects.

Experience

Work history, roles, and key accomplishments

Software Engineering Intern

Hexa Planet Solution

Mar 2023 - Present (2 years 4 months)

Designed and deployed a .NET + SQL backend with embedded ML logic, reducing average processing time by 20% for real-time client queries. Automated data ingestion and validation pipelines, eliminating 2+ hours/day of manual work and ensuring scalable input handling.

.NET SQL Machine Learning Data Pipelines Backend Development Data Validation

Machine Learning Intern

InternshipStudio

Jul 2021 - Present (4 years)

Built predictive ML models using Scikit-learn and Python, enabling platform-level decision-making for 1,000+ users. Trained and fine-tuned a KNN classifier, improving classification accuracy by 20% and achieving a ROC AUC of 0.86.

scikit learn Python Machine Learning Data Visualization