I combine real operations experience with Python, SQL, analytics, automation, and accessibility-focused software development. My strongest work sits between data and operations: ingesting information, normalizing it, maintaining reliable state, automating repeatable workflows, and turning the result into evidence people can act on.
Each project is selected for hiring signal: data pipelines, operations modeling, workflow automation, or disciplined technical research.
Offline-first analytics pipeline that imports structured box-score bundles, validates and stores data in SQLite, derives standings and player/team metrics, and serves reproducible dashboard outputs.
A vending-first operations simulator connecting inventory, vendor ordering, machine loadout, persistent SQLite state, and reporting into one decision-support workflow.
A structured job-discovery pipeline using discover → normalize → dedupe → score → shortlist → export, with SQLite state, deterministic JSON contracts, and safe fixture-backed testing.
Public R&D program investigating assistive computer vision for blind and low-vision users across depth, SLAM, indoor localization, persistent spatial memory, OCR, route planning, uncertainty, and accessible feedback.
The common thread is structured problem solving rather than a single dashboard tool.
I learned systems from the operating side first: inventory, staffing, customers, vendors, cash controls, reporting, and day-to-day problem solving.
Managed stocking, ordering, inventory control, customer service, staffing, scheduling, vendors, sales tracking, and daily operating problems across café and vending workflows.
Supported retail floor operations, supervised and trained a 23-person sales team, tracked performance, and used reporting to identify opportunities for improvement.
Handled sensitive financial records, customer support, and high-volume document migration while maintaining accuracy and confidentiality.
Research repos are explicitly labeled by maturity so research findings are never presented as implemented capabilities.
The public research system separates literature-backed findings, engineering proposals, reproductions, implementations, benchmarks, and controlled field evaluation. Negative results and failure modes are treated as valid engineering evidence.
Primary-source research and technical source mapping.
Reproducibility and measurable validation gates.
Explicit distinction between evidence, architecture, implementation, and safety claims.
Public documentation designed for collaboration and technical review.
The point is not the standings table itself. The hiring signal is the ingestion, derivation, storage, testing, and publication workflow that generated it.
Formal grounding in the analytics process alongside independently built systems and portfolio work.
Public-safe project updates are exported into this build log so visitors can see that the systems are active rather than frozen portfolio pieces.
I am visually impaired, which is one reason accessibility and dependable interfaces matter deeply in my work. Luna is the name of the broader personal engineering environment connecting many of these projects. The professional through-line is simpler: I like understanding how a process works, finding where information or effort is being wasted, and building a better system around it.
Remote-first data operations, reporting, analytics, automation, business systems, or technical support work.
A team where operational understanding and technical problem solving are both useful.
A role where I can keep developing toward deeper data automation and systems engineering responsibilities.
Email is the fastest way to reach me. Resume, LinkedIn, GitHub, and project proof are one click away.
