Job Description

Requisition Number: 73726

The company built on breakthroughs. ​ 
Join us.​ 

Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.  ​ 

How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward. ​ 

​At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.​  

​Come break through with us. 

The global Information Technology (IT) Function is leading efforts to align IT and Business Strategy, leverage IT investments, and optimize end to end business processes and associated information integration technologies. Through these efforts, IT helps to improve the competitive position of Corning's businesses through IT enabled processes. IT also delivers Information Technology applications, infrastructure, and project services in a cost efficient manner to Corning worldwide.

The Junior AI/ML Data Scientist – Generative AI Products is a role supporting Corning’s Enterprise Generative AI platform as well as AI/ML initiatives across the company. This position focuses on building, testing, and supporting data science and machine learning solutions, including forecasting models, analytics workflows, and Generative AI use cases, under the guidance of senior data scientists and engineers.

Responsibilities

• Support development and validation of machine learning and statistical models for business use cases
• Assist in building forecasting and predictive models using established techniques and libraries
• Contribute to Generative AI use cases including prompt development, evaluation, and basic experimentation with LLMs
• Support Retrieval-Augmented Generation (RAG) workflows including document ingestion, embeddings, and retrieval
• Perform data preparation, feature engineering, and exploratory data analysis
• Work with structured and unstructured data from enterprise sources and AI platforms
• Collaborate with software engineers to integrate models into applications and pipelines
• Help document models, assumptions, results, and data pipelines
• Monitor model performance and assist with troubleshooting and improvements
• Participate in agile ceremonies including sprint planning, stand-ups, and retrospectives
• Build scalable AI/ML models for given use cases, including price elasticity and recommendation systems, ensuring applicability/reusability across multiple business units.
• Leverage tools such as Databricks and AWS SageMaker to manage data, build models, and deploy solutions.
• Collaborate with stakeholders, understanding their needs and translating them into AI/ML solutions.
• Continually monitor and evaluate the effectiveness of AI/ML models and forecasting techniques, making necessary adjustments to optimize performance.
• Stay abreast of the latest AI/ML trends and technologies, identifying opportunities for innovation and improvement

Education & Experience

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. Typically requires 2–4 years of experience in data science, analytics, or machine learning roles, or equivalent academic and project experience.

Required Skills

• Proficiency in Python and familiarity with data science libraries (pandas, NumPy, scikit-learn)
• Basic understanding of machine learning algorithms and statistical modeling techniques
• Experience working with SQL and relational or NoSQL databases
• Exposure to cloud or data platforms such as Databricks or AWS
• Familiarity with version control tools such as Git
• Ability to clearly communicate data insights and document work
• Experience working in agile or Scrum-based teams

Desired Skills

• Exposure to Generative AI models or LLM APIs (e.g., OpenAI, Azure OpenAI, Anthropic)
• Familiarity with time-series forecasting methods (ARIMA, Prophet, or similar)
• Basic experience with vector databases or similarity search concepts
• Experience using notebook environments such as Jupyter
• Interest in enterprise AI platforms, data governance, and scalable solutions