Yahya Almardeny

▪ Analysis and modelling of telematics data.
▪ Mathematical derivation and Statistical Analysis of the Online Metrics.
▪ Manipulating high-volume from varying sources to highlight patterns, anomalies, and relationships.
▪ Applying machine learning methodology and techniques.
▪ Analysis and fusion of crowdsourced data.
▪ Producing critical insights for business and product owners to pursue Zero Crashes, Zero Emissions, and Zero Congestion.
▪ Building data visualisations to summarise the conclusion of the advanced Analysis.
▪ Supervising and providing guidance/consultation to other developers and scientists.
▪ Developing and operating a real-time analytics platform for automated driving development.
▪ Building APIs for data consumption and machine learning modelling.
▪ Data Pre-processing, Cleaning and Statistical Analysis.
▪ Collecting and processing big data from a vast fleet of vehicles.
▪ Building distributed data streaming systems and applying considerable data systems enhancements.
▪ Developing real-time deep learning systems ML algorithms.
▪ Supervising and providing guidance/consultation to new developers and engineers.

▪ Delivering modules: Data Structures and Algorithms for two programs: BSc in Computing and NUIST.
▪ Demonstrating the practical implementation and design of the data structures and the algorithms.
▪ Monitoring the students’ performance and providing the required assistance in the class/lab.
▪ Writing the exam questions, interviewing students, and correcting exam answers.
▪ Participating in the board meetings and future planning and curriculum.

▪ Applying feature transformations for machine learning models on new data.
▪ Designing and implementing the ML programs and working on frameworks.
▪ Developing deep learning systems for various use cases based on business needs.
▪ Undertaking machine learning experiments and Fine-tuning test results.
▪ Testing and continuously improving the accuracy of the ML models to ensure optimised performance.

▪ Implementing the sensors models from their datasheets.
▪ Running and testing the Pythonic system on Raspberry Pi and its configurations.
▪ Hardware Connectivity of the devices and sensors on the breadboard.
▪ Developing the server-side and middleware using NodeJS.
▪ Developing the data visualisation interface and its real-time charts for the end-users.
▪ Deploying the system and collecting and analysing data received in real-time.