Welcome to my GitHub.

I’m a Data Scientist focused on building reproducible, decision-oriented data products—from data preparation and statistical analysis to ML/DL modeling, NLP/LLM applications, and analytics engineering that supports trustworthy dashboards and business decisions.

My foundation blends quantitative rigor (Mathematics & Science Education), human-centered analysis (Sociology), and systems + business thinking (MIS + MBA-level coursework), supported by project management discipline (PMI-aligned training) and real-world responsibility through leadership and volunteer work.

• End-to-End Data Science Pipelines (EDA → Feature Engineering → Modeling → Evaluation → Deployment)
• Statistical Analysis, Experiment Design & Model Interpretability
• Machine Learning (Supervised & Unsupervised Learning)
• Deep Learning (Neural Networks; practical model building & evaluation)
• Natural Language Processing (NLP) & LLM Applications (Embeddings, Semantic Search, RAG)
• Analytics Engineering (ELT/ETL, warehouse-friendly modeling, dbt-style transformations)
• BI & Decision Support (KPI design, executive dashboards, stakeholder storytelling)

Note: Tooling listed reflects the environments I’ve used across academic programs and hands-on analytics/ML workflows.

• Regression, Classification, and Clustering
• Feature Engineering, Leakage-Safe Validation, and Robust Evaluation
• Cross-Validation, Hyperparameter Optimization, and Model Selection
• Interpretable ML (feature importance, error analysis, calibration mindset)

• Neural Networks (MLP/CNN/RNN fundamentals; practical training & evaluation)
• NLP workflows (tokenization, embeddings, text classification, similarity search)
• LLM applications: Prompting, tool-usage patterns, RAG, semantic search
• Reproducibility mindset: experiment tracking habits, clean notebooks → scripts

• Analytics engineering (warehouse modeling, clean marts, documented metrics)
• SQL for scalable reporting and trustworthy KPI layers
• Data quality habits (sanity checks, assumptions, reproducible transformations)

• KPI frameworks: clarity, consistency, metric definitions, drill-down design
• Executive-ready storytelling (CEO/CFO views, segmentation, cohorts, trends)
• Dashboard usability: filters/controls, naming, semantics, and interpretation

• B.A. Sociology — Human behavior, society, and qualitative/quantitative perspectives
• B.Sc. Mathematics & Science Education — Strong quantitative reasoning, structured problem solving, pedagogy
• M.Sc. Management Information Systems (MIS) — Systems thinking, business analytics, data-driven strategy
• MBA-level Coursework (within MIS curriculum) — Business Analytics, CRM, Sustainable Management, Big Data Management, Value Chain, Project Management, Research Methods, Low-Code App Development

• PMI-aligned Project Management training (planning, scope, risk, communication cadence)
• Agile mindset (iterative delivery, clear milestones, stakeholder alignment)
• High-responsibility volunteering in Search & Rescue (discipline, teamwork, decision-making under pressure)

I build projects with real-world framing, clean documentation, and reproducible pipelines:

• End-to-End ML Projects (from raw data → validated models → delivery-ready outputs)
• Market Basket Analysis & Recommendation Systems
• Customer Segmentation & Behavioral Analytics
• NLP & LLM-Based AI Applications (Embeddings, Semantic Search, RAG)
• KPI-Driven Dashboards & Decision Support

• Structured thinking + practical execution
• Transparent assumptions, clean pipelines, reliable evaluation
• Strong communication (teaching background; executive-friendly outputs)
• Collaborative and disciplined delivery habits