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ML Engineer with 20+ years of experience
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Assessing your cultural and operational fit
I enjoy building tools and platforms to make ML more accessible. I have authored and am a core maintainer of many popular open source tools: https://hamel.dev/oss/opensource.html Furthermore, I have extensive experience (20+ years) as a machine learning engineer across a wide variety of industries. I am currently helping people operationalize LLM models. I worked on GitHub Copilot, a large language model deployed at scale used by over 100M developers, as well as pre-cursors to Copilot like CodeSearchNet. Professional Page: https://hamel.dev/
Georgia Institute of Technology
Master of Science (M.S.), Computer Science, Machine Learning
N/A – Present
University of Michigan
Doctor of Law (J.D.), Cum Laude
N/A – Present
Southern Methodist University
B.S., Mathematics and Industrial Engineering
N/A – Present
Parlance Labs
AI Engineer & Educator
May 1, 2023 – Present
United States
fast.ai
Entrepreneur in Residence
August 1, 2022 – May 1, 2023
Outerbounds
Head of ML & Data Science
January 1, 2022 – August 1, 2022
fast.ai
Core Contributor & Maintainer
September 1, 2019 – August 1, 2022
GitHub
Staff Machine Learning Engineer
October 1, 2017 – January 1, 2022
San Francisco Bay Area
Airbnb
Senior Data Scientist - Machine Learning
January 1, 2016 – January 1, 2017
San Francisco Bay Area
DataRobot
Senior Data Scientist
January 1, 2015 – January 1, 2016
Greater Boston
AlixPartners
VP, Applied Analytics
January 1, 2011 – January 1, 2015
Greater Boston
Accenture
Consultant - Management Consulting
January 1, 2004 – January 1, 2008
Dallas-Fort Worth Metroplex
Washington Mutual
Credit Risk Analyst
May 1, 2003 – April 1, 2004
Greater Seattle Area
Top 6% Cdiscount Image Classification
December 1, 2017 – Present
Image classification of 9Million Images into 5,000 categories for a French e-commerce website (Cdiscount.com), which is similar to Amazon. Utilized deep learning, Keras and Tensorflow to place 35th out of 627 competitors.
Top 4% Rossman Store Sales
January 1, 2016 – Present
Kaggle Competition: placed in the top 4%. Time series forecasting problem for large German drugstore chain.
Top 25%: Liberty Mutual Property Inspection Prediction
September 1, 2015 – Present
Kaggle competition: Placed in the top 25%. Built a predictive model that predicted the count of hazards or pre-existing damages using a dataset of property information.
Top 25%: Springleaf Marketing Response
August 1, 2015 – October 1, 2015
Kaggle competition: Placed in the top 25%. Using a large set of anonymized features, the challenge was to predict which customers will respond to a direct mail offer. Dataset was extremely wide (~2000 features) which required careful feature selection and engineering.
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit through their diverse project portfolio, including open-source contributions and Kaggle competitions, indicating a passion for continuous learning and innovation. Their experience across various companies, from startups to large enterprises, and in both technical and educational roles, suggests adaptability and a broad perspective. The focus on building tools and frameworks for other data scientists aligns with a collaborative and enabling culture. The target role of 'Data Analyst' might be a slight mismatch given their senior ML/AI engineering and leadership background, suggesting they might be overqualified or seeking a more strategic/architectural data role.
Soft Skills & Operational Fit
The candidate's extensive experience in leadership roles (Head of ML & Data Science, VP, Applied Analytics) and teaching (AI Engineer & Educator) suggests strong communication, mentorship, and team collaboration skills. Their involvement in open-source projects and authoring a book indicates a proactive and collaborative work attitude. The project descriptions highlight problem-solving and strategic thinking, crucial for operational fit.