Description

You will design and prototype solutions by analyzing requirements and historical operational data from aircraft fleets.

Responsibilities

• Apply Prognostics & Health Monitoring techniques to assess component health and monitor failures.
• Develop and refine algorithms to estimate Remaining Useful Life and identify degradation patterns.
• Implement anomaly detection, fault isolation, and health prediction methods.
• Support building and productionizing ML models using modern stacks and MLOps practices.
• Design visualizations and dashboards showing KPIs, model outputs, and uncertainty for stakeholders.

Required Skills

• 9+ years of related work experience in data analytics, machine learning, and AI models.
• Strong understanding of classical machine learning, regression, and classification algorithms.
• Proficiency in Python, Machine Learning, TensorFlow, PyTorch, SciKit-learn, SQL, Predictive Maintenance, Data Analytics, and Artificial Intelligence.
• Experience implementing edge/cloud architectures for fleet-scale telemetry.
• Ability to execute AI testing, verification, and validation with continuous monitoring.
• Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, Physics, or equivalent.

Key Skills

Education

Any Graduate