For educators
Keep assessment intent, rubric evidence, feedback, and professional judgment visible.
About
Mathematics and learning-systems leadership, extended through data and AI.
Mathematics and learning-systems leader who designs AI-enabled, data-informed education systems—combining assessment and measurement, analytics, automation, and implementation to improve student success, instructional quality, and organizational decision-making.
Professional Evolution
The work expands; the throughline stays the same.
My work begins in mathematics and academic leadership: defining learning goals, building assessment systems, interpreting evidence, supporting instructional quality, and helping people act on what the data shows.
I extend that work through analytics, automation, data infrastructure, and AI. Through Logos Education Group, I connect mathematics assessment with reporting and practical program review.
The Assessment Analytics dashboard and Logos department report present the same synthetic scenario in two forms: interactive exploration and a written account of the findings, limits, and next decisions.
Positioning Pillars
Six parts of one practice
- 01Mathematics and academic leadership
Start with the learning purpose, instructional context, and people responsible for the work.
- 02Assessment, measurement, and success metrics
Define what evidence means before building a report, model, or workflow around it.
- 03Data-informed education
Turn learning and operational signals into analysis that supports a clear decision.
- 04AI-enabled systems
Use AI inside explicit evidence, review, safety, and release boundaries.
- 05Technical implementation
Connect LMS/API workflows, analytics, automation, and data infrastructure to the intended use.
- 06Cross-team translation
Make the same system understandable to educators, leaders, analysts, and technical teams.
Working Practice
Leadership and implementation belong together
For leaders
Clarify success metrics, decision thresholds, limitations, and the action a system supports.
For analysts
Make definitions, transformations, validation, and reporting logic inspectable.
For technical teams
Express workflow boundaries, interfaces, tests, data contracts, and release controls clearly.
Role Alignment
Where this work fits
Primary career roles
- Learning Engineer and AI Learning Engineer
- Learning Analytics and Learning Systems Analyst
- Assessment and Measurement Systems
- Education Data Systems and Education Data Analyst
- Assessment Technology and EdTech technical implementation
- Education AI evaluation and selected data/analytics roles
Selective parallel work
Remote, part-time data and domain-expert AI evaluation can complement the primary direction when the work values mathematics, education, measurement, careful review, or analytical judgment.
This is a target-work category, not a claim of prior AI-evaluation employment.
Contact & Opportunities
Let’s talk about learning systems that need both domain judgment and technical implementation.
For learning engineering, assessment, analytics, education data, EdTech implementation, or carefully scoped AI-evaluation work, connect through LinkedIn or review the public source on GitHub.