Aiden Rangel
Software Engineering Intern
6+ years exp
C#
C++
SQL
JavaScript
C
Java
Python
Node JS
TypeScript
Data Pipeline & Feature Engineering: 4/10
Active 1 day ago
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Overview
Technical skills
Timeline
Roles
Overview
UI-focused frontend engineer (Middle) whose main strength is building interactive, DOM-driven browser tooling and no-code style editors. The strongest proven skill is imperative UI construction and dynamic template insertion as implemented in BlueSkyStatistics/ExtensionBuilder8-18/script.js. There is limited public evidence of component-based React development, automated tests, performance measurement data, or advanced state-machine discipline.
Technical skills
C#
C++
SQL
JavaScript
C
Java• 6y+
Python• Middle
Node JS• Middle
TypeScript• Junior
Python
Requests
Node JS
Express
Mongoose
Electron
Databases
PostgreSQL
Supabase
AI/ML
LLM
XGBoost
NumPy
Pandas
Scikit-learn
AI Agents
Frontend
Vite
ESLint
React.js
DevOps
Git
Design
Figma
Mobile
Firebase
QA
Jest
Timeline
Software Engineering Intern
•
Junior
BlueSky Statistics
•
Internship
Worked on a Leading R GUI product used by large international user installs. Improved application startup performance by profiling initialization and removing unnecessary package imports. Built a benchmarking harness for grid loading strategies and implemented grid editing features with bulk undo/redo by correcting backend state handling. Contributed to dataset-wide search across an Electron, Python, and R stack and repackaged backend components with PyInstaller.
Electron
Node JS
Python
Software Engineering Intern
•
Junior
YStem and Chess
•
Internship
Designed and implemented a chatbot interface in a STEM and chess tutoring platform used by thousands of students. Debugged and fixed client-side connection issues in a real-time messaging experience to restore reliable delivery. Added unit tests for UI behavior and navigation flows using Jest and React Testing Library, collaborating via pull requests and iterative review.
React.js
Jest
Middle AI/ML Engineer
Confidence: High Data-centric
Sports-focused data-centric ML developer at a middle level with a strength in time-aware feature engineering for game-level prediction tasks. The strongest proven skill is temporal feature engineering and leakage avoidance demonstrated by features.py where per-team rolling statistics are computed with shift(1) and merged back to game rows. There is limited evidence of production deployment, experiment tracking, test coverage, or advanced efficiency and research work.
Model Architecture & Training
3/10
How well models are designed and trained
Basic, correct model training and evaluation choices (XGBoost baseline, class-imbalance handling, standard scaler and saved model) but no advanced training loops, hyperparameter search, or rigorous experiment tracking.
Data Pipeline & Feature Engineering
4/10
How data is prepared for models
Clear, correct feature engineering and time-aware data pipeline work including per-team game log reshaping, shift(1) to avoid leakage, rolling-window stats, and merging back to game-level rows.
Evidence
AidenRangel12/NCAA-Basketball-Predictor/features.py: team_game_log construction and .groupby(...).transform(lambda x: x.shift(1).rolling(...).mean()) preventing leakage
AidenRangel12/NCAA-Basketball-Predictor/data_collection.py: get_games_for_date and get_season_games with polite sleeps and API usage
Experimentation & Evaluation
3/10
How results are measured and tested
Basic experimentation and evaluation are present with a temporal train/test split, simple metrics and a feature importance plot, but no cross-validation, experiment tracking, or systematic ablation studies.
MLOps & Deployment
2/10
How models are shipped to production
Minimal MLOps: model and scaler are serialized to disk and predict.py loads them for offline use, but there is no serving, versioning, CI/CD, monitoring, or deployment automation.
Computational Efficiency
1/10
How efficiently computing resources are used
Little to no attention to computational efficiency beyond choosing XGBoost; no GPU usage, batching, quantization, or profiling evidence.
Research Depth & Innovation
1/10
Depth of research and new ideas
No research-level contributions or novel algorithmic work; code implements standard approaches without paper reimplementation, ablations, or advanced experimentation.
Evidence
No files implementing custom architectures, novel losses, or research-grade experiments were found in the human-authored files
Verified artifacts
Industries
Sports• Middle
Technologies
SQL
C++
PostgreSQL
Supabase
XGBoost
Scikit-learn
Pandas
NumPy
Git
LLM
Recommendations
- Build automated experiment tracking (W&B or MLflow) and include reproducible run configs to enable systematic hyperparameter search and ablation studies.
- Add unit tests and basic integration tests for data pipeline steps, plus validation checks to harden against API or data schema changes.
- Implement simple serving (Flask/FastAPI or BentoML) with model versioning and basic latency/health checks to make predictions production-ready.
- Introduce cross-validation or time-series-aware backtesting and a small hyperparameter tuning stage (Optuna/Hyperopt) to strengthen claims on model performance.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Backend Developer
Confidence: Medium Data Platform
A Python-focused data engineer at a Middle level specializing in end-to-end sports analytics and lightweight ML pipelines. The strongest proven skill is feature engineering and correct temporal modelling for sports outcomes, demonstrated by features.py use of shift(1) to avoid leakage and train.py's temporal split plus XGBoost training. The public code lacks production-grade APIs, automated tests, resilience patterns, CI/CD, and structured logging required for deployment at scale.
API Design
1/10
How well APIs are designed
Almost no API design; a CLI-style predictor is present but there is no HTTP API, versioning, idempotency, or documented error contract.
Evidence
predict.py: interactive input prompts and check_team_name function
Data Layer & Database
4/10
Working with databases
Solid data-layer work for a single-node pipeline: careful rolling-feature engineering to avoid leakage, temporal train/test split, class imbalance handling, and persisting model and scaler, but no DB schema evolution or migrations.
Evidence
features.py: groupby().transform(...shift(1).rolling(...)) to compute rolling stats and avoid leakage
train.py: temporal split on season, reuse of scaler, scale_pos_weight in XGBClassifier, and saving data/model.pkl and data/scaler.pkl
data_collection.py: get_games_for_date and get_season_games building games dataframe from ESPN API
Scalability & Performance
1/10
Handling load and speed
Minimal scalability or performance engineering; polite rate limiting in scraping exists but no caching, queuing, connection pooling, or measured optimizations.
Evidence
data_collection.py: time.sleep between requests and between seasons
System Architecture
2/10
Overall system structure
Basic modular separation into collection, feature engineering, training, and prediction scripts is present but this is script-level decomposition without service boundaries, config management, or deployment patterns.
Evidence
Project layout: separate modules data_collection.py, features.py, train.py, predict.py
predict.py and train.py persist and read artifacts from data/ directory
Security & Auth
1/10
Protecting data and access
Little evidence of security practices; minimal input validation for numeric inputs only, no secrets management, no dependency audit, and no protection against malformed external API responses.
Evidence
predict.py: try/except around converting rest days to int as only input validation
data_collection.py: direct requests.get calls to ESPN API without retries, timeouts, or auth handling
Reliability & Observability
2/10
Stability and monitoring
Basic observability through print statements and saved artifacts plus numeric evaluation output, but no structured logging, metrics, retries with backoff, or graceful shutdowns.
Evidence
train.py: prints training/testing counts, accuracy, logloss, classification report, saves model and scaler
data_collection.py: prints per-date counts while scraping; predict.py prints user-facing error messages
Expertise
Python• Middle
Backend AI & LLM• Middle
Industries
Sports• Middle
Technologies
Python• Middle
Requests
Recommendations
- Develop and productionize a simple HTTP prediction API around predict.py using a lightweight framework and add input validation, timeouts, and structured logging.
- Add automated tests and a small CI pipeline to validate data ingestion, feature generation, and model reproducibility before deployment.
- Introduce retries with exponential backoff and request timeouts for external API calls, and avoid hard-coded file paths by moving config into environment-aware config files.
- Instrument basic metrics and add model/data sanity checks to detect data drift and failed ingestions.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: Medium UI Engineer
UI-focused frontend engineer (Middle) whose main strength is building interactive, DOM-driven browser tooling and no-code style editors. The strongest proven skill is imperative UI construction and dynamic template insertion as implemented in BlueSkyStatistics/ExtensionBuilder8-18/script.js. There is limited public evidence of component-based React development, automated tests, performance measurement data, or advanced state-machine discipline.
UI Component Architecture
3/10
How interface parts are built
Component architecture is largely imperative DOM templates and ad-hoc functions rather than a deliberate component model; there are reusable template patterns but no React component tree or design-system authored by the developer.
Evidence
BlueSkyStatistics/ExtensionBuilder8-18/script.js: dynamic layout templates and imperative DOM creation using Sqrl.render and insertAdjacentHTML
AidenRangel12/AIChat/index.html: React entrypoint referencing src/main.jsx (project scaffold present but React components not provided in the human-authored files)
Responsive & Cross-browser
4/10
Works on all screens and browsers
Responsive layout and dark-mode are implemented in CSS using media queries and prefers-color-scheme, showing cross-device styling attention though JS does not include advanced feature-detection or RTL/i18n scaffolding.
Performance Optimization
3/10
Speed of the interface
Some pragmatic, small performance-minded choices exist (for example using navigator.sendBeacon to persist state on pagehide), but there is no measured optimization, bundle analysis, or advanced runtime tuning.
Accessibility & Semantics
4/10
Usable for everyone
Basic accessibility and semantics are considered - use of role="tab", controls attributes and focus-visible CSS exists - but custom widgets lack full keyboard handling, ARIA states and tested a11y patterns.
State Management & Data Flow
3/10
Managing data in the app
Client state is managed in a simple in-memory object and persisted to server; server-side proxy has basic validation, but there is no evidence of request cancellation, optimistic updates with rollback, cache strategy or state machines for complex flows.
UX & Visual Polish
4/10
Look and feel quality
Visual polish and theming are present via CSS variables, dark mode and composed templates; UX patterns such as dynamic template insertion and preserved layout state are implemented, but loading/skeleton patterns and consistent perceived-performance techniques are limited.
Expertise
React• Junior
Frontend Architecture & Build Tools• Middle
PWA & Web APIs• Junior
Technologies
JavaScript
TypeScript• Junior
Node JS• Middle
Express
Electron
Figma
React.js
Vite
Mongoose
ESLint
npm• mentioned only
Recommendations
- Assign to build interactive browser tools and no-code editors that require dynamic DOM templating, drag-and-drop and client-side persistence.
- Have them migrate key builder pieces into a component model (React + TypeScript) and introduce modular components and unit tests to improve maintainability.
- Task them with implementing robust client-state management (e.g., React Query or a state machine) and end-to-end tests for the drag-and-drop and persistence flows.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: