Machine Learning Engineer · Forward deployed
I go where the problem is and stay until it works.
I’m Barry (Zehui) Zhang, a Machine Learning Engineer who works next to the people who use the system. At CVS Health/Aetna I took two problems from business operations to systems in daily use: I agreed the contract with the business, built and shipped both, and showed with production metrics that they work. On my own I built two complete products, alone.
Tools: Python · SQL · FastAPI · Kubernetes · Airflow · BigQuery · GCP · TypeScript · PostgreSQL
- Most recently
- CVS Health / AetnaMachine Learning Engineer, Feb 2025 – Nov 2026
- Education
- M.S. Biostatistics and Data ScienceWeill Cornell MedicineB.S. Joint Mathematics-EconomicsUC San Diego
- Research
- Sole author5 manuscripts in preparation, 3 patent applications pending
- Available
- ImmediatelyRemote, or relocating anywhere in the US
From a business problem to a system people use.
- 01
Scope
With the business, at CVS Health/AetnaStarted from two problems in business operations: cases that fell to manual review when a live lookup failed, and review flags that were false positives. Authored the team's AI initiative proposal, phased and with explicit kill criteria.
Read the case - 02
Agree
With the business, at CVS Health/AetnaDesigned the API contract and ran the working sessions with business operations, Data Science and the automation team. Led the weekly sync that set priorities and reviewed results before each release.
Read the case - 03
Build
With the business, at CVS Health/AetnaBoth systems as their end-to-end owner: the pipelines, the services, a data product and the monitoring.
Read the case - 04
Roll out
With the business, at CVS Health/AetnaStaged a breaking change across dev, test and prod and watched adoption until every caller had switched. Nothing broke on the day it became mandatory.
Read the case - 05
Prove
With the business, at CVS Health/AetnaAnswered “is it working?” with daily metrics reconciled to source data, and traced the misses to their cause.
Read the case - 06
Hand over
With the business, at CVS Health/AetnaWrote the engineering handbook the team uses for onboarding and support. The release path became the team's reference pattern. Served as the bridge to a partner organization.
Read the case
At CVS Health/Aetna I was the end-to-end owner of both systems and wrote most of the code in their repositories. I also owned the team's production services and pipelines across dev, test and prod.
Production systems, full products and research.
Work that starts with the people who have the problem and ends with a system they use.
- CVS Health / Aetna · 2025 – 2026
Real-time decision system
A decision model, put into production as a real-time service that automation bots and an AI assistant call.
Real-timedecisions for automation bots and an AI assistant - CVS Health / Aetna · 2025 – 2026
Manual-review reduction pipeline
A text-matching engine and the pipeline around it, which find false-positive review flags in free-text notes and clear them every day.
Dailydelivery that keeps wrongly flagged cases out of manual review - OrchestrationAirflow 2toAirflow 3
- CI/CDJenkinstoGitHub Actions
- CloudOld environmenttoNew environment
CVS Health / Aetna · 2025 – 2026Production platform and migrations
Production services and pipelines across dev, test and prod, kept running while the platform underneath them was replaced.
Dev to prodownership of production services and pipelines, through platform migrations
Independent project · Nov 2026Pawline: a custom pet-portrait gift store
A complete store where a customer uploads a pet photo, places it on a product and approves a designer's proof.
117automated tests across Chromium, Firefox and WebKit
Independent project · Nov 2026Wine retail commerce and operations platform
A bilingual storefront, wholesale ordering and four staff workspaces on one schema, correct under concurrency.
41tables, with role-based access for four staff roles- Independent research · 2025 – present
Risk-aware healthcare cost prediction
Sole-author research on who to reach when capacity is fixed: a multi-task deep-learning model of heavy-tailed healthcare spending.
2.0×high-cost spending captured at top-0.5% capacity - Personal project · 2026
Predicting six-hour Bitcoin moves: a negative result
A blending ensemble for six-hour BTCUSDT moves, taken through a locked test and a costed backtest. The answer was no.
0.514balanced accuracy on the locked test, interval includes 0.5 - Resume
The whole record, on two pages
Experience, research, projects, skills and education. Read it here or take the PDF.
Machine Learning Engineer · web and PDF - This site
How this site is built and checked
The design system, the drawings, and the checks every page has to pass before it ships.
Designed, built and run by me
Figures from my time at CVS Health/Aetna are left out of this site. I am glad to walk through them in conversation.
Who should a care team reach when it can only reach a few?
Healthcare spending is heavy-tailed: a small number of people account for a large part of the total. Most cost models are tuned for the average. This research asks what a model should predict when capacity is fixed and only the top of the distribution can be acted on.
Read about the researchFlagging the top 0.5% of people, the model captures 6.24% of total spending against 3.11% for the baseline (2.01×, p = 0.018).
Count-based top-k% selection, model against a calibrated logistic baseline. Paired cluster bootstrap, B = 2,000.
An engineer with a statistician’s training.
I am a Machine Learning Engineer in Irvine, California. I interned at CVS Health/Aetna in 2024 and returned full time from 2025 to 2026, building the systems that automate decisions that used to need a human.
I studied Mathematics-Economics at UC San Diego and Biostatistics and Data Science at Weill Cornell Medicine. That mix shows up in the work: I like systems that run in production, and I like being able to say how well they run, with an interval around the answer.
- 01
Own the whole path
Design, pipelines, service, rollout, monitoring and support. The hard problems sit at the handoffs between them, so I would rather hold all of them.
- 02
Measure what changed
A system is finished when there is a number that shows it works, and that number has been checked against the source data.
- 03
Bring the callers along
A breaking change is a people problem first. I agree on the contract, stage the change and watch adoption until everyone has moved.
- ML & Modeling
- PyTorch, TensorFlow/Keras, scikit-learn, XGBoost/LightGBM, Optuna
- Serving & MLOps
- FastAPI, Pydantic, Docker, Kubernetes (GKE), REST API design, streaming, rate limiting, GitHub Actions, Jenkins, Snyk, SonarQube, pytest, HashiCorp Vault
- Data & Cloud
- GCP (BigQuery, Airflow on Cloud Composer 2 and 3, GKE, GCS, Cloud Monitoring/Logging, Secret Manager, IAM), AWS
- Observability
- Grafana, Prometheus/PromQL, custom metrics, structured logging, OpenTelemetry, load testing
- Full Stack
- TypeScript, React, Next.js, Node.js, PostgreSQL, Prisma, Playwright
- Languages
- Python, TypeScript, SQL, R, Java, C++, Bash
- Tools
- Codex, Claude Code, Cursor, Git
- Domain
- Healthcare data, RPA integration, HIPAA/PHI-safe design