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itsdanielshay

Dan Shay

@itsdanielshay

Computer Scientist

Estados Unidos
Persa, Inglés
Parte de la información aparece en idioma inglés.
Sobre mí
Hi, My name is Dan Shay and I'm a curious Computer Scientist, and I've developed in different areas in this field for 7 years now. I've been focused on Android and Web development so far, and I've also worked in both Machine Learning development and Information Technology, specially Information Authenticity and Security.... Lee más

Habilidades

i
itsdanielshay
Dan Shay
desconectado • 
Tiempo medio de respuesta: 2 horas

Revisa mis servicios

Desarrollo de aplicaciones para Android
I will develop your android app with kotlin, jetpack compose, etc
DeUSD 100 / proyectoMás información
Asistencia general
I will be your reliable virtual assistant for data entry, email and admin tasks
DeUSD 10 / proyectoMás información

Experiencia laboral

Android Engineer

FlashNest • Trabajador autónomo

Jun 2023 - Jan 2026 • 2 yrs 7 mos

Built and shipped an Android learning app for creating and studying flashcards with AI-assisted tutoring. • Architected an offline-first Android app using MVVM + Clean Architecture (UI/domain/data layers), accelerating feature delivery by 30% and reducing cross-feature coupling by 35% • Designed a reusable Jetpack Compose component system (Material 3, theming, state hoisting), cutting UI regressions by 40% and improving key screen load time by 25% • Built local persistence with Room (schema migrations + indexing) and resilient background jobs via WorkManager, enabling fully offline study and lowering sync-related defects by 60% • Hardened networking with Retrofit + OkHttp (timeouts, retries, caching, interceptors), decreasing failed requests by 35% in poor connectivity conditions • Integrated on-device sentence-transformers embeddings for semantic features (similarity/related concepts), achieving ~60ms median inference latency per query on mid-range devices, and reducing the token cost by up to 85% • Optimized embedding + search pipeline (batching, caching, lightweight indexing), shrinking peak memory by 25% and improving median response time by 45% • Re-architected Android state management and pagination using Kotlin Coroutines/Flow and Paging 3, lowering stale-UI and double-load edge cases by 50% and improving scroll smoothness by 20% • Instrumented crash reporting/analytics to prioritize fixes, lowering crash rate by 55% and sustaining 99.6% crash-free sessions among test users • Automated CI with GitHub Actions (build + unit tests + lint), decreasing regressions by 45% and cutting release verification time by 40% • Improved startup performance with baseline profile + lazy initialization, shrinking cold start time by 20% • Managed release workflows (versioning, ProGuard/R8, Play Console staged rollouts), maintaining 4.7/5 average rating in surveys filled out by test users, and stable rollouts with rollback-ready monitoring

University_of California, Riverside

University of California, Riverside

Tiempo completo • 9 mos

Teaching Assistant

Sep 2025 - Dec 2025 • 3 mos

• Facilitated weekly discussion sections and coordinated grading/rubrics/feedback for 120+ students, maintaining consistent evaluation across groups • Designed assignment specs, rubrics, and exemplars to align with learning objectives, increasing group activity collaboration by 18% and bringing down regrade requests by 30% • Lectured 100+ students in an 80-minute guest session, increased next week’s attendance by 10%, and group activity score by 12% • Streamlined grading workflow (templates + tracking + QA passes), cutting turnaround time from 10 days to 7 days (~30% faster)

Graduate Student Researcher

Sep 2024 - Mar 2025 • 6 mos

• Built Python ETL pipelines (validation + normalization) over multi-source datasets totaling 5M+ rows, saving 70% manual preprocessing time • Applied ML trend analysis and synthesized outcomes into 12 stakeholder-ready research deliverables (reports, figures, summaries) used in ongoing project decisions • Redesigned research website information architecture and navigation, improving content discoverability and lowering bounce rate by 35% • Automated repeatable reporting (plots + tables) for research updates, decreasing weekly reporting effort by 50%