I’m an innovative, business-savvy data professional with an M.S. in Business Analytics & Data Science and a strong foundation in AI/ML. I specialize in building end-to-end analytics pipelines—covering classification, clustering, time series forecasting, and deep learning—using tools like Python, SQL, BigQuery, and Power BI. I'm Tableau Certified and experienced across GCP, SAS, KNIME, and other platforms.
My professional background includes consulting for mid- to large-sized clients, working with enterprise-scale operational data at a Fortune 500 energy company, and leading international project teams in remote areas of Thailand. Across these roles, I’ve built dashboards for executive decision-making, automated data workflows to reduce ETL times by 50%, and navigated multilingual, cross-functional environments with fluency in Thai. Additionally, my machine learning and analytics projects — developed through my M.S. in Business Analytics and Data Science — span agriculture, sports, healthcare, cryptocurrency, and business intelligence domains.
I consistently take initiative to lead—from leading ML projects to founding a startup initiative in remote Thailand. There, I navigated foreign government compliance, led Thai-speaking teams, and scaled operations sixfold under lean budgets. Whether in cross-functional ML teams or international fieldwork, I bring systems thinking, clarity under pressure, and cross-functional team leadership.
I thrive on solving complex problems with creativity and precision, translating raw data into strategic insights that drive innovation and real business outcomes. I'm currently seeking data analyst or data scientist roles where I can leverage my technical skills, leadership experience, and passion for impact.
<aside> ✉️ nzlomke@gmail.com
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Oklahoma State University
GPA: 3.9
Relevant Coursework:
University of Oklahoma
GPA: 3.6
Python
SQL (Structured Query Language)
R
PySpark
Bash
PowerShell
SAS
KNIME
Excel
Microsoft SQL Server
BigQuery
SQLite
Tableau
Power BI
Looker
TIBCO Spotfire
SAS Viya
Predictive Analytics
Machine Learning (ML)
Deep Learning (DL)
Model-Building
Statistical Analysis
Forecasting
Data Modeling
Data Mining
Exploratory Data Analysis (EDA)
