Mark Groner is a data scientist with 10 years of experience who translates complex data into high-impact business decisions across media and technology companies. He has driven measurable gains—from a 4% lift in trial-to-paid conversion at Paramount+ and optimizing allocation of a $250M marketing budget to cutting month-end close time at Meta—while building end-to-end ML solutions and MLOps practices. Comfortable across Snowflake, S3, Python, Streamlit, and production pipelines, he also led migrations to unified data warehouses and coached peers to scale data capabilities. Based in Austin, he pairs a finance and accounting background with formal ML/AI training to bridge analytical rigor and product-focused experimentation. A pragmatic operator, he favors actionable models and tooling that empower non-technical stakeholders to act on insights.
10 years of coding experience
5 years of employment as a software developer
Post Graduate Program Machine Learning and Artificial Intelligence, Post Graduate Program Machine Learning and Artificial Intelligence at The University of Texas at Austin
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