Job
Description
As a Data Scientist at our organization, you will be an integral part of the Flight Safety team, driving safety-related decisions and enhancing operational processes through advanced analytics and machine learning techniques. Your primary focus will be on leveraging time-series and multivariate data to detect anomalies, uncover patterns, and develop predictive models that contribute to flight safety and risk mitigation. Your responsibilities will include performing exploratory data analysis to derive insights from complex datasets, implementing anomaly detection techniques on various data types, and developing machine learning and deep learning models for predictive safety analytics. You will also be tasked with using big data analytics tools and AI/ML frameworks to support data-driven decision-making, designing custom algorithms to meet specific requirements, and writing efficient, maintainable code in Python and MATLAB for data processing and modeling. To excel in this role, you should have a Bachelor's degree in Engineering with a specialization in Data Science or a related field, along with certification in Data Science. A minimum of 6 years of hands-on experience in data science, machine learning, or analytics roles is required, with a proven track record in developing AI/ML models, particularly in anomaly detection and predictive analytics. Key skills that will be essential for success in this position include data analysis, exploratory data analysis, anomaly detection on time-series and multivariate data, machine learning and deep learning techniques (such as ANN, RNN, CNN), big data analytics, AI-driven risk forecasting, strong programming skills in Python and MATLAB, custom algorithm design, and data collection and preprocessing. By joining our team, you will have the opportunity to contribute to cutting-edge data science initiatives in the aviation safety domain, work in a collaborative and innovation-driven environment, and be part of a mission-critical team focused on predictive safety and operational excellence.,
