About
Who I Am
I’m Adam Ezzat — a Data Scientist and Data Analyst based in Cupertino, CA, with a background that spans economics, business analytics, and deep learning. I build machine learning models, design data pipelines, and — in my own time — build programming languages from scratch.
My professional work focuses on forecasting, statistical modeling, and turning messy real-world data into systems that actually make decisions better. My personal work is where I push further: CJC-Lang, a deterministic scientific computing language written entirely in Rust, is the project where everything I’ve learned converges.
Professional Experience
Big Data Insights — Data Scientist
Cupertino, CA | 2023 – Present
Building and deploying ML and statistical models for sales forecasting. I designed forecasting systems using ARIMA, SARIMA, LSTM, and N-BEATS architectures, and developed the company’s core forecasting product around the N-BEATS neural network. I also apply k-NN for historical analog pattern matching and automate end-to-end data pipelines in Python.
Amazon, Lab 126 — Data Collection Specialist, Lab Engineer
Sunnyvale, CA | 2021 – 2023
This is where I learned how much data quality matters — and how hard it is to get right. I ran experiments with audio recording devices and spent as much time diagnosing the data as collecting it. When results looked off, the first question was always: is this an environment issue, a script issue, or genuinely bad data? Tracing problems back through the pipeline — from raw recordings to processed outputs — taught me that clean data isn’t a preprocessing step, it’s the foundation everything else depends on.
Sangu — Data Scientist (Part-Time)
Sunnyvale, CA | 2023
Coordinated data engineering and analytics initiatives. Built PostgreSQL data pipelines, automated log extraction from Linux servers using Perl, and contributed to enterprise CRM data migration projects.
Big Data Insights — Data Analyst
Sunnyvale, CA | 2019 – 2023
Analyzed complex datasets to support product optimization and strategic decisions. Built a K-means clustering model for event location optimization, enhanced data migration with ML techniques, and conducted performance analysis in distributed system environments.
Technical Skills
| Category | Tools |
|---|---|
| Languages | Python, SQL, R, Perl, JavaScript, Bash, Rust |
| ML / Deep Learning | Scikit-learn, TensorFlow, Pandas, ARIMA, SARIMA, LSTM, N-BEATS |
| Cloud & Big Data | Amazon QuickSight, Amazon Redshift, Amazon Glue, Databricks, Spark |
| Analysis & Viz | Tableau, Jupyter Notebook, Microsoft Excel |
| Systems | PostgreSQL, Linux, Git, Rust (compiler engineering) |
Education
- M.B.A. in Business Analytics and Technical Marketing — St. Mary’s College of California, 2018
- B.S. in Economics — Santa Clara University, 2013
- Data Science Certificate (Specialization in Machine and Deep Learning), 2021
The CJC-Lang Project
Outside of my professional work, I’m building CJC-Lang (Computational Jacobian Core) — a deterministic scientific computing language implemented as a 21-crate Rust workspace. It’s where my data science background meets systems programming: a language with built-in tensors, automatic differentiation, statistical distributions, and guaranteed reproducibility. The compiler, runtime, and all core libraries are built from scratch with nearly zero external dependencies.
CJC-Lang is in active beta — the full compiler pipeline, dual execution engines, ~300 builtins, and end-to-end ML benchmarks are all working. It’s a serious technical project, not a toy, but it’s not yet production-ready.
You can read more about it in the blog post or on the projects page.