The AI Guy
Data Science Lead
London, England, United Kingdom
Summary
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RockstarThe AI Guy is a Data Science Lead at a major UK fintech with six years’ experience building production ML systems and mentoring teams to adopt data-driven cultures. Based in London, he combines hands-on computer vision engineering—contributing open-source YOLOv3/4 and DeepSort integrations, GPU-enabled APIs, and license-plate OCR plumbing—with strategic coaching on career transitions from analyst to data scientist. He publishes practical tutorials and teaches the often-overlooked career skills required to build and scale data science capability inside organisations. Comfortable shipping models, APIs, and Colab-friendly workflows, he bridges research, implementation, and organizational adoption to turn ML prototypes into repeatable products.
6 years of coding experience
Github Skills (20)
object-tracking10
python10
tensorflow10
object-detection10
computer-vision10
yolov410
tess4j9
image-processing9
integrate9
integrations9
machine-learning9
ocr9
tesseract9
tesseractjs9
deep-learning9
Programming languages (2)
Github contributions (5)
theAIGuysCode/Object-Detection-API
Feb 2020 - Sep 2020
Yolov3 Object Detection implemented as APIs, using TensorFlow and Flask
Role in this project:
ML Engineer
ML EngineerContributions:58 commits, 2 PRs, 57 pushes in 7 months
Contributions summary:The primarily focused on the implementation and modification of a YOLOv3 object detection API. Their contributions included adding and modifying files related to the model, including the `models.py` file with model definitions and the `detect_video.py` file which implemented object detection on videos. Additionally, the user updated models to function with a GPU and added features such as displaying FPS.
flaskobject-detectiontensorflowyolov3yolo
theAIGuysCode/yolov4-deepsort
Aug 2020 - Aug 2021
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
Role in this project:ML Engineer
ML EngineerContributions:21 commits, 4 PRs, 19 pushes in 1 year
Contributions summary:The contributed to the object tracking implementation using YOLOv4 and DeepSort. They implemented features to allow the user to select specific classes to track and added the ability to display information about each tracked object, including its ID, class, and bounding box coordinates. Furthermore, the user added the functionality to count and display the number of objects being tracked. They also made adjustments to the codebase to allow for easier use within Google Colab environments.
object-trackingtensorflowyolov4yolov4-deepsortdeep-sort
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