Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 4

Successfully implemented an AI-based License Plate Recognition (ALPR) system in over 150 facilities across multiple countries, improving efficiency and accuracy in parking gate operations.
Developed a powerful algorithm with over 99.6% accuracy for recognizing vehicle plate numbers, colors, and make/models, enhancing the performance of the ALPR system.Spearheaded the development of a comprehensive churn prediction pipeline, retaining over 500 clients on a monthly basis and preserving valuable customer relationships. Developed an innovative algorithm integrating real estate prices with proximity to essential amenities, providing a comprehensive scoring system for informed buying decisions. Achieved unprecedented levels of accuracy in depth estimation for 3D reconstruction, reducing the margin of error between estimated depths and ground truth measurements.
At Parqour, as a CV Engineer, the focus was on implementing an AI-based License Plate Recognition (ALPR) system for parking gates across multiple countries. The accomplishments include improving the existing ALPR systems, developing a highly accurate algorithm for recognizing vehicle plate numbers, colors, and make/models, and optimizing the ALPR system's performance on various devices. Additionally, managing a team of computer vision engineers and collaborating with a team to optimize the ALPR system's functionality for different parking areas showcased strong leadership and teamwork skills. In the role of a Data Scientist at KCELL, the main responsibility was to develop a pipeline for churn prediction and forecasting. This involved designing a robust data warehouse, leveraging technologies like Hadoop, Hive, and PySpark to establish an efficient churn prediction pipeline, and successfully retaining clients through effective pipeline implementation. At BI Technologies, the focus was on real estate evaluation and liquidity assessment. The achievements included constructing an extensive database, establishing a robust data warehouse and machine learning pipeline, and achieving impressive accuracy in house price estimation.As an Assistant Researcher at Nazarbayev University, the work primarily involved investigating feature correspondences for 3D reconstruction and depth estimation. At Petrel AI, the main focus was on developing a robust model for vehicle plate number detection and recognition.The work at the University of Maryland involved developing a specialized model for detecting sun angles on crop and face images, aiming to improve the accuracy of image tamper detection. This required conducting in-depth research on CNN architectures and image recognition techniques to design a custom CNN model tailored to sun angle detection. The ability to apply research findings to practical applications demonstrated strong expertise in the field of computer vision.