Data Scientist - Predictive Maintenance and AI for Aeronautical Systems

• Degree in Data Science, Computer Engineering, Aerospace Engineering, Mathematics, Statistics, Physics, or related degrees.
• At least 3 years of professional experience in Data Science, Machine Learning, or Advanced Analytics projects.
• Demonstrable experience in programming with Python and handling libraries such as Pandas, NumPy, and Scikit-Learn.
• Knowledge and experience in Deep Learning frameworks such as TensorFlow and/or PyTorch.
• Experience in processing and analyzing large volumes of data using technologies such as PySpark and Parquet formats.
• Knowledge in time series analysis, predictive maintenance, anomaly detection, prognosis, and remaining useful life (RUL) estimation.
• Experience in data visualization tools such as Matplotlib, Seaborn, or Plotly.
• Knowledge of Git and good collaborative development practices.
• Analytical profile, proactive, problem-solving oriented, and with the ability to work in multidisciplinary teams.

• Experience in industrial environments, Digital Twin, complex systems, engineering simulation, or the aerospace sector will be especially valued.

We are looking for a Data Scientist who will be responsible for the development of advanced predictive analysis solutions for Virtual Engineering and Predictive Maintenance initiatives at Airbus. The selected person will work with synthetic data generated by engineering simulation models, transforming large volumes of information into Machine Learning algorithms capable of anticipating degradation, detecting anomalies, and estimating the remaining useful life of components and systems. A solid foundation in Data Science, Artificial Intelligence, and predictive maintenance techniques is required.

General responsibilities:

• Analysis and exploitation of synthetic data generated by simulation models to identify behavior patterns, trends, and failure modes.
• Design, development, and validation of Machine Learning models oriented towards predictive maintenance, early anomaly detection, prognosis, and Remaining Useful Life (RUL) estimation.
• Definition, development, and evaluation of Health Indicators (HI) and Condition Indicators (CI) to monitor the health status and the evolution of degradation in aeronautical systems.
• Identification of critical variables and characterization of failure modes through advanced data analysis and statistical modeling techniques.
• Design and implementation of data preparation, cleaning, transformation, and exploitation processes to guarantee the quality and reliability of the developed models.
• Evaluation of predictive model performance using metrics and KPIs defined by the client.
• Development of Artificial Intelligence-based solutions applied to Virtual Engineering, Digital Twin, and complex systems environments.
• Close collaboration with simulation specialists and system experts to interpret results, validate hypotheses, and continuously improve the developed models.
• Preparation of technical documentation and participation in project reviews with Airbus teams.
• Contribution to technological innovation through the development of advanced capabilities in condition-based maintenance and predictive analysis.

What we are looking for