Donald Tucker

Industrial Engineer

Applying lean principles and data analysis.

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About Me

Introduction

I am an Industrial Engineer with manufacturing experience of over 15 years. Proficient in applying data analysis and lean principles in process improvement initiatives, cost savings estimates, time studies, capacity analysis, work schedules, rate modifications, staffing requirements, and performance metrics.

I'm currently learning deep learning with Tensorflow

15+ Years
experience

Skills

Technical level

Software

Languages and Applications

Python

75%

SQL

75%

Excel

90%

HTML

50%

CSS

30%

Tableau

75%

Business Objects

80%
more

Data Analysis

Exploratory Data Analysis

80%

Regression

75%

Machine Learning

60%

Data Viz

80%

Time Series Forecasting

70%
more

Industrial Engineering

Lean

Process Improvement, A3

85%

Impact assessments

85%

Metric Reporting

90%

Capacity, Staffing Analysis

85%

Line Balancing

85%

Cost Forecasting

85%

Precedence Network Analysis

85%
more
Skills Diagram (click and drag nodes to explore)

Experience

My personal journey
Education
Work

MS Industrial Engineering

New Mexico State University
2012

BS Industrial Engineering

University of Texas at El Paso
2008

Industrial Engineer Mgr

Lockheed Martin- Aero, F35
11/2022 - Present
More info

Industrial Engineer Mgr

  • Manager leading a team of Industrial Engineers to develop metrics, data analytics, process improvement opportunities, resources allocation, and constraint mitigation plans.


Industrial Engineer Asc Mgr

Lockheed Martin- Aero, F35
10/2019 - 10/2022

Industrial Engineer Stf

Lockheed Martin- Aero, F35
10/2017 - 10/2019

Industrial Engineer Sr

Lockheed Martin- Aero, F35
8/2015 - 10/2017

Industrial Engineer

Lockheed Martin- Aero, F35
3/2014 - 8/2015

Industrial Engineer Asc

Lockheed Martin- Aero, F35
9/2012 - 3/2014

Operations Engineer Asc

Lockheed Martin- MFC, GMLRS
1/2009 - 8/2012
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Accomplishments

Technical achievements

AI Fundamentals Practitioner Badge

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AI Fundamentals Practitioner Badge

  • Random Forest and XGBoost Regressor Capstone Project

  • Artificial Intelligence

  • Machine Learning

  • Hyperparameter Tuning

  • Model Selection


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AI Fundamentals Skills Badge

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AI Fundamentals Skills Badge

  • regression

  • classification

  • natural language processing

  • clustering


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Google Advanced Data Analytics Certificate

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Google Advanced Data Analytics Certificate

  • exploratory data analysis

  • regression models

  • predictive models

  • data visualizations


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Statistics and Probability for Python Skills Badge

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Statistics and Probability for Python Skills Badge

  • distributions

  • hypothesis testing

  • probability


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LM21 Green Belt

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Lockheed Martin LM21 Green Belt

  • Lean Six Sigma

  • DMAIC

  • Process Mapping

  • Data Analysis and Statistical Tools

  • Resource Planning

  • PDCA A3 Problem Solving

Portfolio

Recent projects

Machine Learning Prediction

Random Forest and XGBoost classification model python predictions of survival on a fictitious spacecraft

Check out my notebook

Kaggle Notebook

2 Sample Hypothesis T-test

Utilizing hypothesis testing t-tests to validate process improvements initiatives. Created a template and custom p-value function to interpret the results of the hypothesis tests.

Check out my code

Notebook

NLP Tweet Classification

Binary classification model to determine if tweets are about disaters or not. Uses a TensorFlow Hub pretrained sentence encoder, USE, in a Sequential Model.

Kaggle Notebook

Relational Networks

Visualizing Captain America in the Marvel Character Universe

Hosted Network Diagram in Pyvis
Kaggle Notebook

Tableau Dashboard

Tableau dash built for data exploration in machine learning forecasting model

Machine learning model
Dashboard

Exploratory Data Analysis

Python EDA and data cleaning of store sales

Tableau dash visualization
Juypter Notebook

Housing Price Predictions

XGBoost Regressor predictions of housing prices

Tableau dash visualization
Kaggle Notebook



more projects

Contact Me

Get in touch, send feedback

Email

Location

US