Matthew Carbone
Senior Manager Of Business Analytics at Stand Together
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolMatthew Carbone is a data-driven senior manager of business analytics with over 15 years of experience turning marketing, product and operations data into actionable strategy. Currently leading Business Analytics at Stand Together after directing BI and marketing functions at Inteleos, he blends team-building and data platform architecture to operationalize analytics across web and mobile products. His background spans high-growth SaaS and nonprofit environments where he has driven customer acquisition, experimentation programs, and KPI-driven product decisions that materially increased revenue and conversion. He pairs an MBA in Marketing with hands-on analytics leadership and a pragmatic focus on A/B and multivariate testing, data pipelines, and visualization best practices. An active contributor to open-source ML tooling, he’s improved documentation and reproducibility for a hyperparameter optimization project, signaling an attention to usability and robust experimentation. Based in New York, he is known for translating complex analytics into clear business outcomes while building centers of excellence that scale.
9 years of coding experience
19 years of employment as a software developer
MBA, Marketing, MBA, Marketing at Seton Hall UniversityBachelor of Science - BS, Sport and Fitness Administration/Management, Bachelor of Science - BS, Sport and Fitness Administration/Management at Rutgers University
Github Skills (6)
hyperparameter-optimization10
keras10
deep-learning10
python10
documentation9
tensorflow9
Programming languages (7)
Github contributions (5)
autonomio/talos
Jul 2018 - Jul 2018
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:25 commits, 3 PRs, 75 comments in 8 days
Contributions summary:Matthew primarily contributed to the documentation and structure of the `talos` project, which focuses on hyperparameter optimization for deep learning models. Their work involved adding docstrings to the `scan.py` and `reporting.py` files, improving code comments, and updating format in `reporting.py`. Further, they made changes to the normalizers module and incorporated the ability to seed the random shuffle generator. These edits suggest a focus on code clarity, usability, and reproducibility within the hyperparameter optimization framework.
pytorchhyperparameterdeep-learningoptimizationmachine-learning
matthewcarbone/ScientificValueAgent
Jul 2023 - Feb 2025
The Scientific Value Agent
Contributions:2 releases, 2 reviews, 17 PRs in 1 year 6 months
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