Open to opportunities

Jack Ngam

@jackngam

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NLP and data developer building reliable Python/SQL pipelines from messy data to decisions.

United States

Message

What I'm looking for

I’m looking for a role where I can build reproducible Python/SQL and NLP pipelines, turn messy data into reliable analytics, and collaborate with technical and non-technical stakeholders.

I’m a computational modeling and data analytics graduate with an Economics concentration and a strong focus on turning real-world data into reproducible outcomes. I combine NLP, data engineering, and analytics to make workflows that other teammates can run end-to-end without guesswork.

At Virginia Tech, I worked as an “NLP Research Fellow,” building a Python pipeline that cleaned, deduplicated, and clustered 50,000+ survey records while writing documentation for reproducibility. I kept the full workflow version-controlled in GitHub and Docker, and at Zyglio I built and maintained backend data pipelines in Python and Azure with 100% success and 0% errors across all runs.

My analytics work also shows up in applied settings: in my “Manufacturing Process Analytics Capstone,” I used SQL and Excel Power Query to prepare relational data for Power BI and monitored data quality, tracing root cause for a 38–41% annual decline in process capability. I’m continuing that momentum through projects like PaperTrade (Python, FastAPI, React, WebSockets, SQLite) and “Bank Stress Testing & Capital Adequacy Analysis,” where I built a SQL-backed financial database and reporting framework in Python for a $5B+ portfolio, improving forecast accuracy by 39.95%.

Experience

Work history, roles, and key accomplishments

Current

Full-Stack PaperTrade

Current

PaperTrade

Mar 2026 - Present (5 months)

Built a full-stack system using Python, FastAPI, React, WebSockets, and SQLite, including a 12-table relational database schema with end-to-end decision logging. Implemented logging so data issues can be traced back through the pipeline.

Python fastAPI React WebSockets SQLite Relational Databases Logging Full Stack

NLP Research Fellow

Virginia Tech – GrayUR

Aug 2025 - Dec 2025 (4 months)

Built a Python pipeline to clean, deduplicate, and cluster 50,000+ survey records for an NLP research effort. Version-controlled the workflow in GitHub and Docker to make the analysis reproducible.

Python NLP Data Cleaning Deduplication Clustering GitHub Docker Survey

Manufacturing Process Analytics Capstone

East Penn Manufacturing

Aug 2025 - Dec 2025 (4 months)

Used SQL and Excel Power Query to clean and transform relational data (1.8M rows) and load it into a Power BI reporting layer. Monitored data quality and investigated a 38–41% annual decline in process capability, documenting the resolution for technical and non-technical audiences.

SQL Power BI data transformation Data Quality Root Cause Analysis

Bank Stress Testing Project

Bank Stress Testing & Capital Adequacy Analysis

Aug 2025 - Dec 2025 (4 months)

Built and maintained a SQL-backed financial database and reporting framework in Python, pandas, and Streamlit for a $5B+ portfolio. Transformed and queried data across four portfolios and improved forecast accuracy by 39.95%.

SQL Python Pandas Streamlit Financial Modeling data transformation Forecasting

Software Developer Intern

Zyglio

Apr 2025 - Aug 2025 (4 months)

Built and maintained backend Python and Azure data pipelines supporting database operations with 100% success and 0% errors across runs. Resolved data issues, documented resolutions in a standard format, and tracked configurations for reproducibility.

Python Azure Backend Development Data Pipelines Database Operations Documentation Error Tracking

Operations Analyst

K.S. Kanommongkol

May 2023 - Aug 2024 (1 year 3 months)

Reconciled daily sales, cash, and inventory records across 5–6 sources to keep figures accurate for owner-level decisions. Identified and corrected discrepancies during reconciliation.

Cash Reconciliation Inventory Reconciliation

Education

Degrees, certifications, and relevant coursework

Virginia Tech

Bachelor of Science, Computational Modeling & Data Analytics

Grade: Cum Laude; GPA: 3.52

B.S. in Computational Modeling & Data Analytics with an Economics concentration and a Mathematics & Statistics minor, graduating cum laude with a 3.52 GPA.