Miguel B. Yates
Machine Learning Engineer, Payments
Austin, TX · (978) 555 0161 · name@example.com · linkedin.com/in/miguelbyates
Key Qualifications
EXPERIENCE
Six years in applied machine learning for payments and risk
INDUSTRIES
Payments, consumer financial technology, credit unions
SPECIALTIES
Fraud model training and serving, feature engineering, drift detection
SYSTEMS
Python, PyTorch, scikit learn, MLflow, Amazon SageMaker, Snowflake, Airflow
CREDENTIALS
Amazon Web Services Certified Machine Learning Specialty, member of the Association for Computing Machinery
EDUCATION
Master of Science in computer science, University of Texas at Austin
Results at a Glance
22%
Review rate
180
Shared features
2
Million transactions
$40k
Year in compute
Executive Summary
Machine learning engineer with six years building fraud and risk models for payments companies. Trains models in PyTorch and scikit learn, ships them through MLflow and Amazon SageMaker, and owns monitoring and drift detection for four production models. Works with risk operations on experiment design before any threshold moves.
Signature Achievements
- Replaced a rules only fraud screen with a trained model that cut losses 22 percent at the same review rate.
- Brought median inference latency down from 240 milliseconds to 70 by moving scoring to a batched service.
- Built the feature store that now serves 180 shared features to four modeling teams.
- Set up drift detection that caught a merchant mix shift three weeks before it showed in the loss numbers.
- Ran an experiment across 2 million transactions that settled a two month argument about threshold placement.
- Retired six stale models and their pipelines, saving about 40,000 dollars a year in compute.
Professional Experience
Machine Learning Engineer
Brazos Pay Systems, Austin, TX 2023 to present
Risk modeling team of five inside a payments processor handling 40 million transactions a month.
- Own four production fraud models end to end, from feature engineering through serving and monitoring.
- Train candidate models in PyTorch and scikit learn and register every accepted version in MLflow.
- Design and read out A B tests with risk operations before a threshold or model change goes live.
- Track inference latency and scoring cost per million transactions against the agreed service budget.
Machine Learning Engineer
Sabine Analytics Group, Houston, TX 2021 to 2023
Consulting team delivering credit and collections models for regional banks.
- Delivered seven client models with documented training data, evaluation results and handover notes.
- Built Airflow pipelines that refreshed training tables nightly across three concurrent engagements.
- Wrote model cards for every delivered model so client risk committees could review them.
Data Scientist
Lone Star Mutual Savings, Austin, TX 2020 to 2021
Small analytics team at a credit union of 120,000 members.
- Built the first member churn model to reach production and handed it to the member services team.
- Automated a monthly reporting extract that had taken an analyst three days.
- Ran the loan default scoring refresh and documented the process for the analytics team.
Licensure and Certification
Amazon Web Services Certified Machine Learning Specialty
Member of the Association for Computing Machinery
Education
Master of Science in computer science, University of Texas at Austin, 2020
Bachelor of Science in mathematics, Texas State University, 2018
Core Skills
Python · PyTorch · scikit learn · Feature engineering · Model serving · MLflow · Amazon SageMaker · Drift detection · Experiment design · Snowflake · Airflow