Exploratory Data Analysis (EDA):
- Perform deep analysis of complex datasets to uncover patterns, trends, and insights relevant to flight safety.
Anomaly Detection:
- Apply advanced techniques to detect anomalies in time-series and multivariate data, identifying potential safety risks.
AI/ML Model Development:
- Design and implement machine learning and deep learning models (e.g., ANN, RNN, CNN) for predictive safety analytics.
Big Data Analytics:
- Leverage big data platforms and AI/ML frameworks to support real-time, data-driven decision-making in safety-critical applications.
Custom Algorithm Development:
- Create and fine-tune algorithms tailored to domain-specific requirements with a focus on accuracy, performance, and reliability.
Programming & Deployment:
- Write efficient, maintainable code in Python and MATLAB for data processing, model training, and deployment.
Data Engineering:
- Collect, clean, and preprocess data from diverse sources to ensure the integrity and quality of inputs used in model development.
Collaboration:
- Work closely with cross-functional teams to convert analytical insights into actionable initiatives for improving flight safety.
Required Qualifications & Experience:
- Educational Background:
- Bachelor's degree in Engineering with a specialization in Data Science or a related discipline.
- Certification in Data Science (completed course or equivalent recognized program).
Professional Experience:
- Minimum 6 years of hands-on experience in data science, analytics, or machine learning.
- Proven expertise in developing AI/ML models, with strong focus on anomaly detection and predictive analytics.
