Sr Staff Data Scientist job opportunity at GE Aerospace.

Bachelor's (B.Sc.)

United States Of America

As a Sr Staff Data Scientist, you will lead and work within teams as a technical domain expert addressing statistical, machine learning, and artificial intelligence problems in a commercial technology and consultancy development environment. You will be part of a data science or cross-disciplinary team driving AI business solutions involving large, complex data sets. Potential application areas include time series forecasting, machine learning regression and classification, root cause analysis #RCA, simulation and optimization, large language models, and computer vision. The ideal candidate will be responsible for developing and deploying machine learning models in production environments. This role requires a strong technical background, excellent problem-solving skills, and the ability to work collaboratively with data engineers, analysts, and other stakeholders. Job Description Roles and Responsibilities: Understand business problems and identify opportunities to implement data science solutions. Develop, verify, and validate analytics to address customer needs and opportunities. Design, develop, and deploy machine learning models and algorithms Work in technical teams on the development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics. Develop and maintain pipelines for Retrieval-Augmented Generation (RAG) and Large Language Models #LLM. Collaborate with data scientists to optimize RAG and LLM pipelines for performance and accuracy. Utilize semantic and ontology technologies to enhance data integration and retrieval. Ensure data is semantically enriched to support advanced analytics and machine learning models. Interact with cloud services and develop and deploy models within cloud environments such as #AWS, Azure, Google Cloud, and Databricks Perform exploratory and targeted data analyses using descriptive statistics and other methods. Work with data engineers on data quality assessment, data cleansing, data analytics, and model productionization Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes. Communicate methods, findings, and hypotheses with stakeholders. Mentor colleagues in technical areas and drive standardization across the analytics enterprise Review data science/AI projects for technical rigor