Product Manager with 9 years of experience blending product strategy and hands-on data science to deliver measurable outcomes. Proven contributor to high-visibility GitHub projects (including Microsoft’s sql-server-samples) where they built and deployed ML models—linear regression for demand forecasting, clustering with the Elbow method, and sentiment analysis pipelines—bringing a data-first approach to product decisions. Comfortable translating ML prototypes into product-ready features and collaborating across engineering and analytics teams to operationalize models. Known for making technical trade-offs pragmatic and customer-focused, with a knack for turning complex data signals into clear roadmaps.
Azure Data SQL Samples - Official Microsoft GitHub Repository containing code samples for SQL Server, Azure SQL, Azure Synapse, and Azure SQL Edge
Role in this project:
Data Scientist
Contributions:49 commits, 20 PRs, 4 pushes in 2 years 10 months
Contributions summary:NelGson contributed significantly to the development and implementation of machine learning models within the repository. Their work involved creating and utilizing Python scripts for training, serializing, and predicting rental counts, specifically using a linear regression model. Furthermore, the user designed and implemented a Python-based customer clustering model, including the utilization of the Elbow method for optimal cluster determination. The contributions also span the use of machine learning services for sentiment analysis of product reviews, demonstrating expertise in both model creation and deployment for predictive tasks.
Contributions:131 commits, 24 pushes in 1 year 6 months
pythonsql-serversqlr-and-pythonsqlite
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