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Tier-1 employer, popular Data Scientist title, metro location, and mid-level role amplify competition.
Core ML/AI skills are transferable, but aerospace domain knowledge moderately limits cross-industry fit.
Mandatory advanced degree plus required Python, SQL, cloud, Docker and CI/CD increases filtering strictness.
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Develop and implement AI and machine learning models to solve business problems within GE Aerospace.
Execute data science projects with limited guidance, ensuring data quality and performance of deployed models.
Collaborate with data engineers, architects, and stakeholders to translate business needs into technical AI solutions.
Master’s or PhD in Statistics, Machine Learning, Computer Science, or related STEM field with analytics development experience.
Proficiency in Python and SQL is mandatory.
Familiarity with Python web frameworks, cloud platforms (AWS, Azure, Google Cloud), Docker, and CI/CD pipelines.
Work Experience Required: Not explicitly mentioned in the JD.
Experience in Generative AI/LLM technologies and knowledge of related AI/ML models (CART, SVM, RF, Neural Net).
Skilled in applying analytics techniques including statistics, feature extraction, predictive analytics, and data visualization for stakeholders.
Ability to deliver components of AI/ML roadmaps, perform data quality assessments, and operate with minimal supervision in a collaborative engineering environment.