Chieh-An Chang
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Chieh-An (Andy) Chang

Data Science & Data Engineering co-op candidate building analytics pipelines, machine learning models, and AI applications from messy data to deployable systems.

Chieh-An (Andy) Chang

About

Building reliable data and AI systems with statistical judgment.

Hello, I am Chieh-An Chang, also known as Andy. I am pursuing a Master of Data Science and Artificial Intelligence (Co-op) at the University of Waterloo after graduating with High Distinction from the University of Toronto in Computer Science and Statistics.

My work sits across Python, SQL, ETL pipelines, Databricks, Snowflake, Azure, data visualization, machine learning, statistical modeling, RAG systems, and agentic AI applications. I am especially interested in turning messy data into reliable systems that can be evaluated, shipped, and improved.

I am seeking Fall 2026 co-op and internship opportunities where data analysis, data engineering, machine learning, and AI application development meet real business workflows.

Technical focus

From messy data to deployable systems

My work connects statistics, data engineering, machine learning, and full-stack application delivery, with projects acting as the practical evidence.

Data

Ingest, clean, model, and document source data.

Modeling

Explore features, train models, evaluate uncertainty.

Engineering

Turn notebooks into repeatable APIs, jobs, and UI workflows.

AI App

Ship usable interfaces with feedback loops and guardrails.

Capabilities

Skills grouped by practical focus

Data Science

Python

Advanced

Data cleaning, modeling workflows, notebooks, APIs, and automation.

Pandas / GeoPandas

Advanced

Data cleaning, transformations, geospatial joins, aggregation, and EDA workflows.

R / Statistical Modeling

Advanced

Regression, ANOVA, Bayesian inference, survey sampling, and statistical reporting.

Data Engineering

SQL

Advanced

Relational modeling, analytics queries, joins, normalization, and database-backed apps.

Databricks / Snowflake

Advanced

Cloud data engineering, warehouse workflows, and enterprise analytics pipelines.

Cloud

Microsoft Azure

Intermediate

Cloud data solutions for analytics, storage, and workflow integration.

Docker / AWS / Vercel

Intermediate

Containerized development, cloud deployment basics, and serverless-friendly releases.

Machine Learning

Scikit-learn

Advanced

Supervised learning, clustering, preprocessing, model selection, and evaluation.

SciPy / Statsmodels

Intermediate

A/B testing, forecasting, statistical validation, and model diagnostics.

AI Engineering

LangChain / LangGraph

Intermediate

Agent routing, state schemas, tool calling, RAG chains, and human-in-the-loop workflows.

LLMs / RAG / Vector Search

Intermediate

Transcript-grounded retrieval, vector stores, prompt workflows, and applied AI interfaces.

PyTorch

Intermediate

Deep learning prototypes, LSTM models, tensors, and neural network experimentation.

Visualization

Power BI / DAX

Intermediate

Interactive dashboards, dynamic measures, Power Query transformations, and market analytics.

Matplotlib / Seaborn / Plotly

Intermediate

Clear exploratory and explanatory charts for technical and non-technical readers.

Frontend

React / Next.js

Intermediate

Reusable UI components, App Router pages, and interactive portfolio surfaces.

Backend

Supabase / Postgres

Intermediate

Database-backed content, auth-aware reads and writes, RLS, and storage-backed assets.