Machine learning for retail loss prevention.
Developing models for fraudulent self-scanning patterns and connecting the modeling work with production systems.
Role: Senior Data Scientist. Scope: Model development, backend contribution and stakeholder understanding. Selected technologies: Python and Google Cloud.
This work comes from Eslam’s previous role in retail technology.
The context
The AI and data team worked on detecting fraudulent retail behavior. Data engineers and data scientists collaborated around patterns in self-scanning shopping trips.
Eslam’s contribution
Eslam worked on feature engineering and model development, alongside backend engineering contributions. The work connected models with the wider system and the product requirements around them.
His responsibilities in this role also included stakeholder dashboards and mentoring data scientists.
The delivered scope
The work included the design and deployment of machine learning systems for shoplifting prevention. It connected model development, engineering and the people responsible for the product.
A manager’s perspective
He played a key role in developing our machine learning-based solution for loss prevention.
Jan Morten Kvistnes · Direct manager · December 2024
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Where this experience is relevant
Machine learning initiatives that need both model development and integration with an operating product, with clear communication across data, engineering and product teams.