Building and verifying software across complex systems.
Software engineer and SDET with seven years at General Motors spanning backend and full-stack development, test automation, systems analysis, and production delivery. My work has included Java services, REST integrations, and React applications. I am continuing formal study in applied data science.
Developed Java microservices, middleware, and full-stack features for automotive inventory, vehicle configuration, and owner-experience platforms using Spring, Quarkus, React, TypeScript, PostgreSQL, Kafka, Redis, Elasticsearch, Azure, Docker, and Kubernetes.
Built REST endpoints and React/TypeScript pages for an inventory dashboard feature involving aged vehicle inventory and associated dealer data.
Traced missing inventory records through source delivery, API and service logic, and caching layers; found valid source variations that the application had discarded.
Created and maintained automated REST API, UI, and mobile tests using Java, Rest Assured, Selenium, SoapUI, Postman, JavaScript, Groovy, and CI/CD pipelines.
Analyzed requirements, workflows, and data flows with product, engineering, and quality teams to clarify behavior and resolve integration issues.
Led quality activities for more than 45 production releases, using release reporting and KPI dashboards to communicate progress, risk, and readiness.
Supported 16 iOS and Android releases across six vehicle brands and 30 markets through testing, defect investigation, release coordination, and delivery reporting.
PITSS America
Technical consultant
Performed business analysis, development, testing, and documentation for Oracle Forms, Reports, and PL/SQL applications serving a state education accountability office.
Investigated student score and reporting discrepancies against education-accountability rules, and documented validation findings for the client and third-party auditors.
Supported a large Oracle Forms upgrade and a WordPress redesign and content-consolidation project using PHP, HTML, and CSS.
02 / EDUCATION
Education
IN PROGRESS
Master of Applied Data Science
University of Michigan
13 credits completed as of 2026
2024
Postgraduate Program in Data Science and Business Analytics
The University of Texas at Austin
2016
BS in Information Technology
Oakland University
03 / LEARNING
Learning across software and data
Applied in professional work
At GM, I worked with inventory data flows, SQL-backed applications, mapping across source systems, discrepancy investigation, and delivery dashboards. At PITSS, I investigated student score and reporting discrepancies against education-accountability rules.
Continued study
The UT Austin postgraduate program gave me structured practice in Python, statistics, and machine learning. In Michigan's Master of Applied Data Science program, I have continued with data systems, visualization, causal inference, and model evaluation. Independent study has also been a consistent way for me to refresh foundations and explore new tools.
Current interests: the overlap between reliable software, data quality, and clear visual explanations of results, along with thoughtful uses of AI and large language models in learning and software development.
04 / COURSEWORK
Coursework
THE UNIVERSITY OF TEXAS AT AUSTIN
Coursework case studies
Seven studies from my completed postgraduate program in Data Science and Business Analytics at the University of Texas at Austin. Each page explains the supplied data, methods, and takeaways, with charts and a link to the full HTML notebook export.
My Master of Applied Data Science is in progress. The 13 completed courses span mathematical foundations, Python and data systems, visualization, inference, data mining, and machine learning.
Practice, math & Python
SIADS 501
Being a Data Scientist
Problem framing, data quality, validation, uncertainty, and the judgment needed to communicate useful results to stakeholders.
SIADS 502
Math Methods I
Linear algebra, probability, optimization, statistical inference, and the mathematical foundations of regression models.
SIADS 505
Data Manipulation
Pandas data cleaning and reshaping, joins, groupby operations, time series, regular expressions, and reproducible preparation.
SIADS 515
Efficient Data Processing
Linux workflows, debugging, Python data structures, generators, caching, algorithmic complexity, and profiling.
Scalable data & databases
SIADS 516
Big Data Scalable Data Processing
Distributed processing concepts, MapReduce, Spark RDDs and DataFrames, Spark SQL, and operations such as grouping and joining at scale.
SIADS 611
Database Architectures and Technologies
Relational and non-relational tradeoffs, PostgreSQL JSON and full-text features, indexing and query performance, and Elasticsearch.
Exploration & visualization
SIADS 521
Visual Exploration of Data
Using charts and statistical context to investigate distributions, patterns, and anomalies before drawing conclusions.
SIADS 522
Information Visualization I
Perception, task fit, visual encodings, and interactive visualization, including basic views built with Altair.
Inference & data mining
SIADS 532
Data Mining I
Representing data as itemsets, matrices, and sequences for similarity, pattern discovery, retrieval, clustering, and outlier detection.
SIADS 630
Causal Inference
Selection bias and research designs for estimating effects, including matching, instrumental variables, regression discontinuity, and differences in differences.
SIADS 632
Data Mining II
Sequence and language models, time-series patterns and forecasting, and methods for data that arrives as a stream.
Machine learning
SIADS 542
Supervised Learning
Regression and classification methods, train-validation-test splits, cross-validation, evaluation metrics, tuning, data leakage, and fairness.
SIADS 543
Unsupervised Learning
Clustering, dimensionality reduction, density estimation, topic modeling, embeddings, and the limits of interpreting structure without labels.