Design, develop, and deploy machine learning models tailored for aviation & automotive industry applications.
Experiment with various algorithms and techniques to improve model accuracy and performance, including supervised, unsupervised, and reinforcement learning.
Collect, clean, and pre-process large and complex datasets from diverse sources, ensuring high quality and relevance.
Perform exploratory data analysis (EDA) to identify patterns, trends, and insights that inform model development.
Deploy machine learning models into production environments, ensuring seamless integration with existing systems and workflows.
Monitor model performance in real-time, managing updates and retraining to maintain effectiveness and accuracy.
Work closely with data scientists, software engineers, and product managers to understand business requirements and translate them into technical solutions.
Communicate complex technical concepts and findings to non-technical stakeholders in a clear and actionable manner.
Stay up-to-date with the latest advancements in machine learning, AI, and industry 4.0 technologies.
Optimize machine learning algorithms and models for performance, scalability, and efficiency.
Implement best practices for model evaluation, tuning, and validation.
Strong programming skills in languages such as Python, R, or Java.
Proficiency with machine learning frameworks and libraries, including TensorFlow, PyTorch, scikit-learn, Keras, or XGBoost.
Experience with data manipulation and analysis using tools like Pandas, NumPy, and SQL.
Solid understanding of machine learning algorithms, model evaluation metrics, and advanced statistical tools & techniques.
Familiarity with cloud platforms such as AWS, Google Cloud, or Azure for machine learning services.
Knowledge of regulatory requirements and compliance standards in the aviation & automotive industries (e.g., ICAO, FAA, GDPR) is a plus.
Familiarity with big data technologies and distributed computing frameworks (e.g., Apache Spark).