Scientific Software Developer & Python Trainer
loganthomas.dev | logan.thomas005@gmail.com | 321.961.9107
I am a highly-motivated and naturally curious individual with experience using Python, Spark, SQL, R, and Data Science/Machine Learning techniques. I exhibit an analytical and detail-oriented nature, while placing strong value on building relationships.
- Programming Language – Python, SQL, Spark, R, BigQuery (Google Cloud Platform)
- Methodology – Data Munging, Data Mining, Machine Learning, Scripting, Automation, Web Development
- Data Visualization – Matplotlib, Plotly, Spotfire, Tableau
- Data Science Advisor – Individual mentorship & advisement
- SciPy 2021
- SciPy 2022
- What Every R&D Leader Needs to Know About ChatGPT and LLMs
University of Florida, Gainesville, FL
Master of Science Mechanical Engineering (Minor in Statistics)
May 2014
Palm Beach Atlantic University, West Palm Beach, FL
Bachelor of Science Mathematics (Minor in Biblical Studies)
Summa Cum Laude
May 2012
Enthought, Austin, TX
Scientific Software Developer & Python Trainer
February 2021 - Present
- Apply machine learning and deep learning expertise to consulting projects
- Teach machine learning, deep learning, and Python focused courses
- Solve technical problems through efficient, idiomatic, and unit tested code
SciPy Conference Tutorial Co-Chair, Austin, TX
January 2022 - Present
PyTexas Committee Member, Austin, TX
January 2023 - Present
DataCamp, Remote
Data Science Course Instructor
February 2019 - October 2022
- Designed interactive online course for data scientists focused on writing efficient Python code here
- 4 hours of content (15 videos with 53 exercises)
- 95,000+ course participants
- 4.7 / 5 average course rating
- Included in curriculum for:
- Data Engineer with Python (Career Track)
- Python Programmer (Career Track)
- Python Programming (Skill Track)
- Python Toolkit (Skill Track)
Protection Engineering Consultants, Austin, TX
Senior Associate Data Scientist
August 2019 - February 2021
- Developed an evolutionary algorithm library for deploying multi-gene genetic programming and symbolic regression (
DEAPandSymPy) - Enhanced computer vision algorithms for autonomous fragment tracking (
OpenCV) - Led Python projects and promoted Git usage within the team
- Security Clearance as of Sept 2020
Nielsen, Austin, TX
Machine Learning & Algorithms Team - Lead Data Scientist
October 2018 - August 2019
- Developed and implemented machine learning models to drive data-driven insights
- Identified patterns and anomalies in large datasets to optimize decision-making processes
- Integrated diverse datasets to provide actionable insights for stakeholders
- Promoted best practices including clean code, version control, and unit testing to ensure quality outcomes
Nielsen, Austin, TX
Machine Learning & Algorithms Team - Senior Data Scientist
July 2017 - October 2018
- Launched state of the art automation engine leveraging network analysis, community clustering, maximum bipartite graph matching, and term frequency-inverse document frequency (TF-IDF)
- Deployed automated data preparation pipeline and deep learning LSTM model using Databricks platform, AWS, TensorFlow, and Keras
- Oversaw model development/evaluation for cookie classification techniques comparing XGBoost, AdaBoost, and other machine learning classifiers
- Engineered end-to-end software solution for Total Ad Ratings product utilizing random forest models, k-d trees, and convex optimization
- Developed R&D data analysis pipeline for viewer assignment project leveraging Databricks platform and Apache Spark
Columbia University School of Professional Studies, New York, NY
Applied Analytics in an Organizational Context - Course Facilitator Associate
September 2016 - December 2016
- Facilitated lessons on Data Science, Open Source, and Modern Analytics (approximately 20 students)
Nielsen, San Francisco, CA
Digital Product Team - Senior Data Scientist
August 2016 - July 2017
- Supported development/improvement of digital measurement products
- Deployed methodological enhancements to foundational machine learning models: age correction model, cookie classification models, and invalid traffic techniques
- Implemented Agile framework with App Dev and Engineering teams to create production level code
Nielsen, Tampa, FL & San Francisco, CA
Emerging Leaders Program - Data Science
July 2014 - August 2016
Rotation 4: Watch Product Enhancement Team (San Francisco, CA)
February 2016 - August 2016
- Lead Analyst for Total Content Ratings (TCR) research leveraging new Data Matching System
- Created, implemented, and enhanced code using SQL and R programming languages
- Coordinated development of data quality assurance checks for implementation of TCR research
- Authored two description of methodology papers that illustrate and annotate TCR enhancements
Rotation 3: Technology & Telecom Team (San Francisco, CA)
August 2015 - February 2016
- Voiced unique opportunities available to Verizon from Nielsen’s diverse Telecom product line
- Streamlined client communication procedures through self-developed technological updates
- Developed comprehensive knowledge of Telecom databases through supporting Solutions & Analytics Team
Rotation 2: Audio Sample Acquisition Team (Tampa, FL)
January 2015 - August 2015
- Documented sampling procedures across Nielsen product portfolio (Television, Audio, and Scarborough)
- Led business wide analysis of sample de-duplication procedure
- Presented list of key findings, best practices, and potential solution ideas to key stakeholders
Rotation 1: Behavioral Methods Team (Tampa, FL)
July 2014 - January 2015
- Performed cost-analysis of TV Diary incentives resulting in $500,000 worth of savings
- Led cross-functional team in evaluation of CATI logic within scripts used for phoning households
- Developed informative FAQ website offered to respondents