William Komp

About William Komp

An experienced data scientist/leader with proven track record delivering bottom line value through R&D with strategic focus — drawing from a physics PhD background, and expertise developing machine learning systems integrating data architecture, systems of model metadata management, building & scoring integration into larger software platforms. Technical editor for J Wiley and Sons in the area of Predictive Analytics and contributor to Predictive Analytics Times, Panelist for Virtual Phd in the area of Science Technology Engineering and MathematicsSpeaker and Moderator Predictive Analytics WorldConference Organizer Midwest Relativity ConferencePeer reviewer:Relative Compadre Physics ArchiveCanadian Journal of PhysicsWGBH Boston Physics Learning Archive, Skills:Languages: Python, SQL, SparkEnvironments/Tools: AWS (Redshift, EC2, S3, RDS, Lambda, Glue, Athena, EMR, Quicksight, EventBridge), Azure (Databricks, ADLS, Synapse, MSSQL, Spark, Data Factory, Powerbi), Google Cloud (Colab, Postgres)SDLC: Jira, Confluence, git hub, Azure Devops, bitbucket, gitlab, Scrum Agile, Clickup, AsanaBackend Development: Data Lake design, Data pipelines, Database Architecture and ManagementBusiness Vertical Experience: Agriculture Commodities, Marine, Chemical Supply, Food & Beverage, Travel & Hospitality, Marketing, Medicare, Health Insurance, Oil and Gas, Transportation, Logistics, IOT, Renewable Energy, Industrial IOT, Finance, Banking, AutomotiveTechnical Experience: Production OCR, Text Mining (dictionary/bag of words), Numerical Differential Equations, Dynamical Data modeling, Monte Carlo/Markov Chains, Graph Theory, Deterministic and Probabilistic Models, Web Analytics and Data Mining (Geospatial analysis, web scraping/mining), Speech Recognition/Text to Speech, Epidemiology models (Logistic Growth, Competitive Hunter, Probabilistic models), Association Rules Mining, Predictive Model Algorithms (Neural Networks, Decision Tree Algorithms, Linear Regression, Logistic Regression), Record Linkage, Multi-parameter optimization, Differential Equations (ordinary, partial, linear and non-linear), Differential Geometry, Bayesian Statistics, Statistical Modeling, Linear Algebra, Set Theory, Inner product spaces, Feature Engineering/Importance, Data Geometry development, Data Lake design and implementationMisc: Latex, 3D printing, OPENFOAM, Q-Blade, FreeCAD

Experience

1. Senior Ai Ml Bi Platform Architect

   GE Aerospace

   Jul 2025 — Present · Cincinnati, OH, US

   Leading Data Architecture and Lake house design and build out.Developed platform behavior models to track platform reliability and anomalous performance. This uses historical models to predict outliers in run and queuing time.Pursuing certifications in line with company standards.Technology: AWS (Lambda, Athena, Iceberg, Glue, Redshift, Quicksight, EMR), Python, Dataiku, Spotfire

Education

• University of Wisconsin-Milwaukee

  Doctor of Philosophy (PhD), Theoretical and Mathematical Physics

• University of Chicago

  Master of Science, Physical Sciences

• University of Louisville

  Bachelor of Science, Physics and Mathematics

Skills

• Fortran
• Statistical Modeling
• Healthcare Analytics
• Financial Analysis
• Proposal Writing
• Matlab
• Wind Measurements
• Data Mining
• Machine Learning
• Social Network Theory
• C
• Programming
• Text Mining
• Statistics
• Python
• Latex
• Differential Equations
• Datamining
• C++
• Relational Data Modeling
• Financial Modeling
• Advanced Analytics
• Web Mining
• Sql
• Analysis
• Predictive Analytics
• Physics
• Analytics
• Fraud Detection
• Data Analysis
• Probability Theory
• Relational Databases
• Algorithms