Oluwaferanmi Wale-Bogunjoko

Data Scientist | Cloud Data Engineer | Full-Stack Developer

+12403672085 · feranwb@gmail.com · Github.com/Maws7140 · State College, PA

Professional Summary

Results-driven Data Scientist and Cloud Engineer with expertise in big data analytics, cloud infrastructure, and full-stack development. Proven track record of architecting complex systems, contributing to open-source projects (78+ merged PRs), and implementing scalable data pipelines. Specializing in Apache Spark, Azure cloud services, and machine learning with hands-on experience processing millions of records and building production-ready applications.

Education

Pennsylvania State University | State College, PA
Bachelor of Science in Data Science | August 2022 – Present
Relevant Coursework:

• DS 410: Big Data Analytics (MapReduce, Spark, RDD, Clustering, PCA)
• CMPSC 461: Programming Language Design & Implementation (Lexical Analysis, Parsing, Lambda Calculus, Type Systems, Memory Management)
• DS 220: Data Science Foundations
• CMPSC 360: Discrete Mathematics for Computer Science
• CMPSC 221: Data Structures & Algorithms
• STAT 414,415, 380, 184: Advanced Statistics, Probability & Statistical Inference
• MATH 230: Calculus & Analytical Geometry

Certifications

• Azure Fundamentals (AZ-900) — March 2025
• AWS Certified Cloud Practitioner — July 2025

Professional Experience

Cloud & Data Engineering Intern

Covenant Global Services (CGS) | May 2025 – August 2025 | Maryland
Azure Cloud Infrastructure:

• Automated provisioning of Azure Storage Accounts and Azure Data Factory using infrastructure-as-code with Azure DevOps pipelines
• Created and configured self-hosted integration runtimes for seamless data orchestration across cloud and on-premises systems
• Implemented Azure Policies and managed enterprise-wide user access controls via Active Directory
• Managed application secrets and credentials using Azure Key Vault with automated rotation policies
  Networking & Security:
• Codified network security by deploying Network Security Group (NSG) rules for secure inter-server communication on port 22
• Performed network diagnostics using NS Lookup for troubleshooting connectivity and DNS resolution issues
• Secured platform access by integrating Informatica with Azure AD using SAML-based Single Sign-On (SSO)
• Extended and managed expiring application credentials across multiple enterprise systems
  Data Analytics & Optimization:
• Leveraged Kusto Query Language (KQL) for advanced analytics on Azure cost management datasets, identifying opportunities for resource optimization
• Worked with Azure Monitor to provide actionable right-sizing recommendations, reducing infrastructure costs
• Analyzed cloud resource usage patterns to improve efficiency and cost-effectiveness
  Technologies: Azure (Data Factory, Storage, DevOps, Policies, Key Vault, NSGs, Monitor), Kusto Query Language (KQL), PowerShell, ARM Templates, Terraform, Active Directory, Informatica

Python Developer

Freelance (Upwork) | June 2025 – Present | Remote

• Built a multi-exchange trading bot integrating Robinhood and Schwab APIs with automated order management, risk controls, and real-time reporting dashboards
• Implemented robust error handling and failover mechanisms for high-reliability trading operations
• Developed real-time monitoring and alerting systems for trading activities

Technical Projects

UFC Sell-Through Prediction & Card Optimization | Big Data Analytics

Status: Completed |Fall 2025
Technologies: Apache Spark, PySpark, Spark MLlib, Spark SQL, Python, Google Trends API, Reddit API, Slurm
Overview:
Developed a comprehensive big data analytics system to predict UFC ticket sales by analyzing fighter impact on event attendance, processing 2.8 million+ records across 430MB of raw data.
Data Engineering:

• Built end-to-end ETL pipelines using PySpark DataFrames with advanced SQL and window functions for feature engineering
• Integrated multiple data sources:
  • UFC event schedules and fighter statistics
  • Google Trends data (2.3M rows) for market interest metrics
  • Reddit sentiment analysis (380K rows) for fan engagement
  • Betting odds and venue capacity data
• Optimized data storage using Parquet format for efficient distributed querying and processing
• Implemented schema harmonization across disparate data sources
  Machine Learning:
• Engineered 36+ features including:
  • Fighter performance metrics (win percentage, finish rate, average fight time)
  • Experience factors (days since last bout, career longevity)
  • Style contrast indicators (striker vs wrestler matchups)
  • Rivalry and rematch indicators
  • Market interest and sentiment metrics
• Trained GBTRegressor and GLM models with cross-validated hyperparameter tuning using Spark MLlib
• Implemented feature importance analysis to identify key drivers of ticket sales
• Calibrated predictions by venue capacity for realistic sell-through estimates
  Optimization & Deployment:
• Developed card optimizer using greedy algorithm with 2-opt swaps to maximize predicted attendance
• Conducted scalability analysis with 1/2/4 executor benchmarking on Penn State ICDS Roar cluster
• Deployed on distributed computing cluster via Slurm with Spark Adaptive Query Execution across 2 nodes
  Deliverables:
• Predictive model with comprehensive feature importance analysis
• Venue capacity calibration system
• Card optimization algorithm for fight placement

Obsidian Storyteller Suite | Open-Source Plugin Development

Status: Published | Spring 2025
Technologies: TypeScript, JavaScript, Obsidian Plugin API, vis-timeline, esbuild, Vitest, CI/CD
Repository: GitHub
Overview:
Core contributor to comprehensive dashboard plugin for writers, published to Obsidian community plugin store with 10,000+ downloads. Merged 78+ pull requests (PR #38 through #78+), demonstrating sustained open-source contribution and collaborative development.
Feature Development:

• Architected modular system for character management, location tracking, event/scene organization, and chapter management
• Built reference library system for language creation, prophecies, and creative inspiration with multi-book support
• Engineered portable data persistence layer using YAML and Markdown for cross-platform compatibility
• Implemented interactive timeline visualization with custom calendar support:
  • BCE/CE date parsing with custom calendar systems
  • Custom month and day configurations
  • Event timeline rendering with drag-and-drop functionality
    Technical Achievements:
• Developed multilingual support for Chinese, English, and other languages with proper translation display
• Resolved critical mobile UI bugs on iOS and Samsung devices, improving cross-platform reliability
• Fixed complex issues including:
  • Timeline gridlines and rendering bugs
  • Timezone handling for global users
  • Fuzzy matching for entity searches
  • Schema harmonization across plugin versions
• Maintained backward compatibility across major version updates
• Established modern development workflow with Vitest for unit testing and esbuild for efficient bundling
  Impact:
• Published to official Obsidian community plugin marketplace
• Actively maintained with ongoing bug fixes and feature enhancements
• Enhanced visual hierarchy and user experience based on community feedback
• Built engaged user community with responsive support

Study Group | AI-Powered Study Hub (Hackathon Project)

Status: Completed |Fall 2024
Technologies: React, FastAPI, Python, Supabase, PostgreSQL, LangChain, LlamaIndex, FAISS, ChromaDB
Overview:
Designed and implemented intelligent, private study hub for college classes with AI chatbot assistance, enabling secure student collaboration while maintaining data privacy.
Full-Stack Architecture:

• Frontend: Built responsive user interface with React for student and professor dashboards
• Backend: Developed microservices architecture using FastAPI (Python) with RESTful API endpoints
• Database: Implemented Supabase (PostgreSQL) with Row-Level Security (RLS) policies for multi-tenant data isolation
• AI/ML Pipeline: Integrated LangChain/LlamaIndex for Retrieval-Augmented Generation (RAG)
• Vector Store: Deployed FAISS and ChromaDB for semantic search on class documents
• Authentication: Implemented secure authentication and authorization using Supabase Auth
  Key Features:
• Student signup system with unique class codes and role-based access control
• Secure document upload and management with cloud storage integration (Supabase Storage)
• AI chatbot indexed exclusively on class-specific documents, preventing cross-contamination between classes
• Professor control panel with customizable guardrails for AI responses
• Enterprise-grade security with encryption, RLS policies, and access controls
  Technical Implementation:
• Designed database schemas with normalized tables and RLS policies for data isolation
• Built RAG pipeline with document ingestion, chunking, embedding, and retrieval
• Implemented real-time updates and notifications for collaborative features
• Created comprehensive API documentation and testing suite
  Impact:
• Production-ready system with finalized architecture and integrated AI services
• Demonstrated ability to rapidly prototype and build complex full-stack applications

UFC Fight Outcome Prediction | Full-Stack ML Application

Status: Completed | Spring 2024
Technologies: Python, Scikit-learn, Flask, Azure App Service, GitHub Actions

• Developed full-stack web application achieving 78% cross-validated accuracy using dual-model ensemble classifier
• Designed bias-elimination system using confidence-weighted averages from two distinct scikit-learn models (Random Forest, Gradient Boosting)
• Exposed prediction engine via RESTful Flask API with JSON endpoints for predictions
• Automated deployment to Azure App Service via GitHub Actions CI/CD pipeline
• Implemented comprehensive testing suite for model validation and API testing

Java Course Scheduler | Data Management Application

Status: Completed | Fall 2022
Technologies: Java, SQL, Apache Derby, JDBC

• Architected data management application applying Object-Oriented Programming (OOP) principles to model students, courses, and schedules
• Designed relational SQL schema with normalized tables for efficient data storage and retrieval
• Managed data persistence via JDBC with prepared statements and transaction management
• Implemented CRUD operations and complex queries for schedule optimization

Flashly | Spaced Repetition Flashcard System

Status: Published | 2024-2025
Technologies: TypeScript, React, FSRS Algorithm, OpenAI/Anthropic/Google Gemini APIs, Jest
Repository: GitHub
Overview:
Developed and published comprehensive spaced repetition flashcard plugin for Obsidian, transforming notes into intelligent study materials using advanced learning algorithms.
Key Features:

• Multiple Flashcard Formats: Implemented support for Q::A inline syntax, ?? multi-line format, {cloze} deletion, and header-based cards
• FSRS Algorithm Integration: Leveraged Free Spaced Repetition Scheduler (FSRS) algorithm, reducing review volume by ~20% compared to traditional SM-2 methods
• Intelligent Deck Organization: Built automatic deck creation from filenames, custom naming via frontmatter, and subtag-based hierarchies
• Flashcard Browser: Created comprehensive deck overview with searching, filtering, and direct study sessions with progress tracking
• AI-Powered Quiz Generation: Integrated multiple LLM providers (OpenAI, Anthropic, Google Gemini) for automated question generation alongside traditional rule-based quizzes
• AnkiConnect Integration: Enabled direct sync to Anki without CSV export workflows
• Interactive Tutorial: Designed guided onboarding for card creation, vault scanning, and review workflows
  Technical Implementation:
• Built with TypeScript/JavaScript and React for UI components
• Implemented Jest testing framework for quality assurance
• Created customizable parsing tags, scheduler algorithms, and daily study limits in plugin settings
• Developed keyboard shortcuts for fast review navigation
  Impact:
• Published to Obsidian Community Plugins marketplace for one-click installation
• Licensed under 0BSD (public domain) for maximum accessibility
• 77 commits demonstrating iterative development and refinement

Additional Active Projects

Apple Reminders Bridge | Obsidian Plugin (Research Phase)

• Designing two-way synchronization between Obsidian and Apple Reminders via AppleScript
• Planning features: custom code block rendering, checkbox toggling, filtering by list/due date, reminder creation
• Technologies: TypeScript, AppleScript, node-osascript, Obsidian Plugin API
  Portfolio Website | Personal Portfolio (Early Stage - 5% Complete)
• Designing retro TV-themed portfolio with remote control aesthetic
• Planning AI chatbot integration for project Q&A
• Technologies: React/Next.js, modern CSS frameworks

Technical Skills

Programming Languages

• Proficient: Python, TypeScript/JavaScript, SQL, React, R
• Experienced: Java, Kusto Query Language (KQL)
• Familiar: AppleScript, Functional Programming (Lambda Calculus, Currying)

Big Data & Analytics

• Frameworks: Apache Spark (RDD, DataFrames, MLlib), PySpark, MapReduce
• Data Processing: Spark SQL, Window Functions, Lazy Evaluation, Data Partitioning
• Analytics Tools: Pandas, NumPy, Statistical Modeling
• Query Languages: SQL, Spark SQL, Kusto Query Language (KQL)

Machine Learning & AI

• ML Frameworks: Spark MLlib, Scikit-learn
• Models: Gradient Boosting (GBT), Generalized Linear Models (GLM), Random Forest, Regression, Clustering, PCA
• AI/RAG Systems: LangChain, LlamaIndex, Retrieval-Augmented Generation
• Vector Databases: FAISS, ChromaDB, Weaviate, pgvector
• Feature Engineering: Temporal analysis, statistical features, domain-specific metrics (36+ engineered features)

Cloud Platforms & Infrastructure

• Azure: Data Factory, Storage, DevOps, Policies, Key Vault, NSGs, Monitor, App Service, Active Directory
• AWS: Foundational knowledge (Cloud Practitioner certified)
• Infrastructure as Code: Terraform, ARM Templates, PowerShell scripting
• DevOps: Azure DevOps, CI/CD pipelines, GitHub Actions, Docker, Git version control
• Distributed Computing: Slurm, Penn State ICDS Roar cluster

Databases & Storage

• Relational: PostgreSQL, Apache Derby, SQL Server
• Cloud Databases: Supabase (PostgreSQL with RLS)
• Vector Databases: FAISS, ChromaDB, Weaviate, pgvector
• Data Formats: Parquet, JSON, YAML, CSV
• Database Design: Schema modeling, normalization, indexing, Row-Level Security policies

Web Development

• Frontend: React, Next.js, HTML/CSS, Mobile-responsive design, Progressive Web Apps
• Backend: FastAPI, Flask, RESTful API design, Microservices architecture
• Authentication: Supabase Auth, OAuth, SAML, Single Sign-On (SSO), Row-Level Security (RLS)
• API Integration: REST APIs, third-party API integration (Robinhood, Schwab, Google Trends, Reddit)

Development Tools & Practices

• Version Control: Git, GitHub (78+ merged PRs on production project)
• Testing: Vitest, unit testing, integration testing, CI/CD testing
• Build Tools: esbuild, Webpack, npm/yarn
• Plugin Development: Obsidian Plugin API, cross-platform development
• Documentation: Technical writing, API documentation, user guides

Data Collection & Processing

• Web Scraping: BeautifulSoup, requests, Firecrawl
• APIs: REST API integration, rate limiting, authentication
• OCR: Document text extraction, field identification
• Natural Language Processing: Sentiment analysis, text classification

Security & Compliance

• Encryption: End-to-end encryption, KMS, Azure Key Vault
• Access Control: Role-based access control (RBAC), Row-Level Security (RLS)
• PII Protection: Data anonymization, secure credential management
• Network Security: NSG rules, VPN, secure network segmentation
• Compliance: Audit trails, SOC 2 considerations

Specializations

• Cloud Data Pipelines: ETL design, data engineering, workflow orchestration
• Distributed Computing: Spark, MapReduce, cluster computing, scalability optimization
• RAG Systems: Document ingestion, chunking, embedding, retrieval, LLM integration
• Plugin Development: Obsidian ecosystem, modular architecture, backward compatibility
• Multilingual Software: Localization, internationalization (i18n)
• Mobile Development: Cross-platform UI, iOS/Android compatibility

Domain Knowledge

• Big Data Processing: Distributed systems, lazy evaluation, data partitioning, query optimization
• Programming Language Theory: Lexical analysis, parsing, type systems, lambda calculus, memory management, OOP
• Database Design: Schema modeling, normalization, RLS policies, transaction management
• Network Administration: Security groups, VPN, DNS, network diagnostics
• Sports Analytics: Demand forecasting, entertainment industry metrics, betting odds integration
• Statistics: Probability theory, statistical inference, hypothesis testing, clustering, dimensionality reduction (PCA)
• Cloud Architecture: Microservices, serverless, infrastructure as code, cost optimization

Professional Attributes

Open-Source Contributions

• Active maintainer with 78+ merged pull requests on production plugin with 5,000+ users
• Published plugin to official Obsidian community marketplace
• Responsive community support and issue resolution
• Collaborative development with distributed teams

System Design & Architecture

• Proven ability to architect full-stack applications from concept to deployment
• Experience with microservices, distributed systems, and cloud-native architectures
• Balanced approach to technical complexity and practical implementation
• Focus on scalability, maintainability, and user experience

Problem Solving

• Demonstrated expertise debugging complex issues:
  • Mobile UI compatibility (iOS, Samsung)
  • Timezone handling for global users
  • Distributed computing optimization
  • Schema harmonization and data migration
  • Performance optimization for large datasets
• Systematic approach to root cause analysis and solution implementation

Technical Communication

• Extensive documentation of complex topics (lambda calculus, distributed systems, programming language theory)
• Clear API documentation and user guides
• Ability to explain technical concepts to non-technical stakeholders
• Active participation in code reviews and knowledge sharing

Rapid Learning

• Quick adoption of diverse technologies across big data, cloud platforms, AI/ML frameworks, and web development
• Self-directed learning through coursework, projects, and hands-on experience
• Ability to deep-dive into new domains (sports analytics, tax systems, creative writing tools)

User-Centric Development

• Balance of deep technical implementation with focus on user experience
• Accessibility considerations in UI development
• Responsive to user feedback and iterative improvement
• Mobile-first and cross-platform thinking

Academic Interests & Continuous Learning

Current Studies:

• Big Data Analytics: MapReduce, Spark optimization, distributed computing patterns
• Programming Language Design: Type systems, compiler construction, formal verification
• Statistics: Advanced probability, statistical inference, machine learning theory
• Cloud Architecture: Cost optimization, security best practices, infrastructure design
  Study Materials:
• Probability and Statistical Inference (10th Edition)
• Programming Language Pragmatics (4th Edition)
• Comprehensive course notes on lambda calculus, parsing, memory management
• Distributed computing and big data processing documentation

Career Focus & Interests

Primary Focus Areas:

• Data Science and Engineering
• Cloud Data Platforms and Infrastructure
• Full-Stack Product Development
• Open-Source Software Development
  Industries of Interest:
• Big Data Analytics and Business Intelligence
• Cloud Computing and Infrastructure
• AI/ML Applications and Platforms
• Developer Tools and Productivity Software
  Professional Goals:
• Build scalable data pipelines and analytics systems
• Contribute to impactful open-source projects
• Design and implement production-ready AI/ML solutions
• Develop user-facing applications with strong technical foundations

Additional Information

GitHub: github.com/Maws7140
Location: State College, PA (Open to relocation/remote opportunities)
Work Authorization: Authorized to work in the United States
Website: KING MAWS | Retro Portfolio

References and project portfolios available upon request