Scott Mackay is a seasoned cloud and architecture leader with 11+ years at UnitedHealth Group, currently directing cloud services and platforms that span public CSPs, Azure Stack private cloud, DBaaS, and enterprise DevOps toolchains. He excels at decomposing complex programs into reusable, maintainable architectural building blocks and stepping into delivery gaps to drive high‑impact initiatives like cloud provisioning automation and security dashboards. A hands‑on former architect and .NET practice lead, he blends deep implementation experience—CI/CD, SDKs, integrations with SaaS like Salesforce—with team mentorship and global delivery management. Based in Salt Lake City, he brings a rare combination of long-term strategic roadmap ownership and practical code-level refinements (including hands-on ML work in an LSTM example on GitHub), which keeps his architecture both approachable to engineers and resilient at scale.
11 years of coding experience
29 years of employment as a software developer
AS Computer Information Systems, AS Computer Information Systems at SLCC
Minimal, clean example of lstm neural network training in python, for learning purposes.
Role in this project:
ML Engineer
Contributions:5 commits, 3 PRs, 5 comments in 1 day
Contributions summary:Scott primarily contributed to the development of an LSTM neural network in Python. Their work included refactoring the code to use Python 3 print functions and removing unused variables. The user also moved the sigmoid and tanh inverse/derivative functions into separate functions and renamed them for clarity. These changes suggest a focus on refining the core LSTM implementation.
This program simulates the attack patterns done of multiple classes in the game OrbusVR. This includes different attack types and the triggering of tilesets.
Contributions:62 commits, 6 PRs, 51 pushes in 2 years 1 month
fltktilesetpatternstilesetsattack
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