Position: Python Developer
Category: Data Science
English level: fluent
Years of experience: 4

- Reduced model deployment time in Azure by over 15% for organisations
- Compiled best practices when compressing conversational AI models using a variety of methods
- Introduced continuous monitoring (using explainability in Azure) for a client's machine learning models reducing customer complaints
- Developed an end-to-end Named Entity Recognition project (from dataset creation, cleaning up to annotation using Spacy and Label Studio) for training students
I was a part of a 3-man team that applied machine learning to reduce insurance claims response time. I leveraged the MASK RCNN model and Tensorflow to build a car damage detection and segmentation system. This model was deployed to Azure and its API consumed from the client application.

I led a team to investigate the effects of the Lagos Okada (Motorcycle) ban and compile a data-driven report for stakeholders in the transport industry. I used Python's Tweepy and Networkx libraries to collect and analyse data related to the ban then visualised using Matplotlib/Seaborn. The project also released a comprehensive report showing the effect of a lack of a last-mile transportation for residents of Lagos State.

Most of work currently relates to Natural Language Processing. I am currently investigating the effects of compression on Conversational AI models and best practices when compressing these models. I am looking for opportunities to deploy these models for use in innovative ways that solve business challenges.