Albert Carter
Director Of Research, Product & Engineering at Bank.Green
Nijmegen, Gelderland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolAlbert Carter is a Director of Research, Product & Engineering with 13 years of experience building data engineering, ML/AI, and NLP systems, currently leading engineering at Bank.Green while also working as a data engineer for Working Spirit. He combines hands-on implementation of ML pipelines—contributing to open-source model versioning and experiment tracking in the VertaAI/modeldb project—with product and research leadership that bridges experimental systems (notably at MIT CSAIL) to production data workflows. Trained in Information Systems (M.S., Northeastern) with a background in organizational behavior (B.A., Brown), he pairs technical depth with team and process insight, often serving as scrum master for environmental groups. Known for practical integrations and refactoring to make ML tooling easier to adopt, he focuses on making complex AI systems auditable, reproducible, and mission-aligned.
12 years of coding experience
Bachelor of Arts (B.A.), Organizational Behavior Studies, Bachelor of Arts (B.A.), Organizational Behavior Studies at Brown UniversityMaster of Science (M.S.), Information Systems, Master of Science (M.S.), Information Systems at Northeastern University
English, Vietnamese, Dutch
Stackoverflow
359reputation
14kreached
4answers
12questions
Github Skills (16)
model-management10
python10
scikit-learn9
scikit9
machine-learning9
pandas7
objective-c6
ajax6
uipageviewcontroller6
ios6
nsfilemanager6
database-administration6
django-views6
feature-engineering6
database6
Programming languages (8)
Github contributions (5)
VertaAI/modeldb
Feb 2017 - May 2017
Open Source ML Model Versioning, Metadata, and Experiment Management
Role in this project:
ML Engineer
ML EngineerContributions:55 commits, 18 PRs, 56 pushes in 3 months
Contributions summary:Albert primarily contributed to the development and integration of machine-learning model versioning and experiment management within the `vertaai/modeldb` repository. Their commits demonstrate the implementation of a Python sample using `modeldb` to track and log model training, metrics, and data used. The user also made modifications to the underlying configuration and scripts related to the model versioning process. This involves modifying existing sample code as well as refactoring and updating the configuration to make it easier to integrate into other Python-based projects.
data-sciencedeep-learningmachine-learningmodel-versioningmlops
CSAIL-LivingLab/getfit
Sep 2014 - May 2015
Contributions:388 commits, 181 pushes, 14 branches in 8 months
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