Harshul Shah

Background

About Harshul Shah

Somewhere between my first line of Python and my first football dataset, I realized numbers don’t just describe the world—they shape it. I started out in Electronics & Communication Engineering in India, with a minor in Computer Science, where I developed a systems mindset—how things work, talk, and fail. But it was during my Master’s in Statistics & Data Science at UT Dallas that I began working at the intersection of structured data, real-world complexity, and applied machine learning. That journey led me to projects that taught me to think like an engineer and a storyteller. At FC Dallas, I automated ETL pipelines using Python and SQL to deliver weekly player insights using GPS and performance data—freeing up time for analysts and giving coaches data they could actually use. I built classification models using Scikit-learn that helped identify player development patterns, turning instinct into evidence. At Gameplay Inc, I engineered a GCP-based data pipeline that transformed facility records and satellite images into a structured database and an analytics-ready interface—bringing together APIs, BigQuery, and geospatial logic. Along the way, I’ve grown fluent in tools like GCP, Git, Power BI, Tableau, and PostgreSQL. I’m confident working with machine learning techniques like Random Forests, Logistic Regression, SVMs, and clustering. More importantly, I know how to put them to work in real systems. But what’s defined my growth most are the soft skills: — Translating technical details into decisions that non-technical stakeholders understand and trust. — Working across teams and time zones, syncing priorities in fast-paced environments. — Staying curious, coachable, and open to feedback, even when it’s uncomfortable. I believe good models are explainable. Good pipelines are reproducible. And good analysts are good listeners. Now, I’m looking for full-time roles where I can bring that mindset to bear—as a Data Scientist, ML/AI Engineer, Data Engineer, or Football/Soccer Analyst. Let’s build things that work—and that matter. Technical Proficiency- Python programming with a focus on data manipulation (Numpy, Pandas) and computer vision applications (OpenCV)- SQL, Tableau - Skilled in C and C++ for algorithmic implementation and software development.