Georgina Wager-Jones
Many organisations do not fully understand their data requirements or AI needs.
About
Many organisations do not fully understand their data requirements or AI needs. My role is to ensure that you understand the options available to you and your business, and to help you identify the most suitable and cost-effective solutions.
I am Georgina Wager, a data scientist and PhD researcher specialising in AgriTech. My work combines video processing, computer vision (including YOLOv8 and DeepLabCut), and statistical analysis (R and Python) to predict individual feed intake and improve animal welfare through automated feeding-behaviour monitoring. I have contributed to multidisciplinary teams at Harper Adams University and with industry partners, helping to develop practical, affordable video-recording systems and scalable pipelines for real-time animal monitoring applicable across all sectors.
Beyond research, I have hands-on experience delivering data-driven insights, developing labelling protocols, and designing ethograms for feeding and drinking behaviours. I enjoy translating complex machine-learning methods into actionable tools and collaborating with researchers, developers, and stakeholders to validate data and models for real-world applications.
- 8 years' experience
- Joined January 2026
Languages
- EnglishFluent
Industry experience
- Agriculture & Mining
- Software & Internet
- Education
- Life Sciences
- Professional Services
More experts in Birmingham, United Kingdom
Work experience· 6 roles
Scoped product feasibility and design trade-offs for a multi-camera livestock tracking system by producing a literature review and strategic technical brief. Synthesised methods from academic and commercial research into clear system design recommendations (rotated bounding boxes, 3D camera integration, calibration pipelines). Outlined edge-case risks (occlusion from camera overlap, signal interference in RFID systems) and scalability considerations. Delivered actionable recommendations guiding tracking system architecture and real-time animal monitoring. Contributed to model evaluation for a Body Condition Score (BCS) predictor using 22,000+ labelled images, addressed class imbalance, and proposed augmentation and targeted data collection strategies. Planned labelling protocols and documentation for pipeline stages including metadata integration and alert triggers.
Tuberscan Project involvement; data analysis; assistant/support lecturer on data science modules; researcher on Modern Slavery Grant; published dataset in Dryad (Investigating human repeatability of a computer vision-based task to identify meristems on a potato plant).
Database and system implementation and management; certification (NICEIC, CHAS, ECA); business development and analysis; Health and Safety Officer; website development and marketing.
Education
Certifications and awards· 8
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