Spencer Schaber
Lead AI Engineer at Blattner
Greater Minneapolis-St. Paul Area United States
Summary
👤
Senior🎓
Top SchoolSpencer Schaber is a Lead AI Engineer with a PhD from MIT and nine years of hands-on experience turning plant-floor and lab data into measurable EBITDA and sustainability gains across global food and energy projects. He has led teams up to 13 people to deliver $20M+/yr in value across 29–31 plants, scaling pilots—like computer vision and physics-enabled soft sensors—into multi-site production while building operational guardrails and model monitoring. Equally comfortable in code and the C-suite, he shortens build cycles by bridging mechanistic simulation (Aspen) with Python, influences $100M–$380M capital decisions, and drives cross-functional adoption through pragmatic upskilling for thousands of R&D staff. His academic rigor (five peer-reviewed papers, KDD poster) informs production-grade solutions that save energy, water, and cost, and he contributes to well-known open-source deep learning educational repos demonstrating practical neural-net expertise. Spencer combines technical depth with a subtle talent for “high-trust hard conversations,” leaving teams more motivated while delivering measurable operational impact.
9 years of coding experience
8 years of employment as a software developer
MSCEP & PhD, Chemical Engineering, MSCEP & PhD, Chemical Engineering at Massachusetts Institute of TechnologyB.ChE, Chemical Engineering, B.ChE, Chemical Engineering at University of MinnesotaInternational School of Geneva
English, French, Spanish
Github Skills (12)
jupyter-notebook10
machine-learning10
deep-learning10
tensorflow10
python10
transfer-learning9
neural-network9
convolutional-neural-networks9
computer-vision8
batch-normalization8
natural-language-processing7
nlp7
Programming languages (5)
Github contributions (5)
udacity/deep-learning
Feb 2017 - Jun 2017
Repo for the Deep Learning Nanodegree Foundations program.
Role in this project:
ML Engineer
ML EngineerContributions:22 commits, 6 PRs in 3 months
Contributions summary:Spencer primarily contributed to the Udacity Deep Learning Nanodegree repository by making revisions and improvements to Jupyter notebooks, focusing on concepts related to deep learning and neural networks. This included fixing typos, clarifying explanations, and correcting image sizes within tutorials. Their changes centered around the `intro-to-tflearn`, `intro-to-rnns`, `transfer-learning`, `language-translation`, `gan_mnist`, `dcgan-svhn`, `face_generation` and `batch-norm` directories, which suggests familiarity with various deep learning models and concepts. The user demonstrates a solid understanding of practical application in neural networks.
pytorchdeep-learning-nanodegreenanodegreefoundationsdeep-learning
schaber/tensorflow
Apr 2017 - Dec 2018
Computation using data flow graphs for scalable machine learning
Contributions:2 PRs, 2 pushes, 1 branch in 1 year 7 months
computationscalabledata-sciencemachine-learninggraphs
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.