Title: Data Scientist (Remote) Location: Corning, NY, US, 14831 Workplace: remote Department: Accounting/Finance/Planning/Audit Job Description: Requisition Number: 74528 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.  Corning’s businesses are ever-evolving to best serve our customers, industries, and consumers. Today, we accelerate and transform life sciences, mobile consumer electronics, optical communications, display, automotive, and solar markets. We are changing the world with: - Trusted products that accelerate drug discovery, development, and delivery to save lives - Damage-resistant cover glass to enhance the devices that keep us connected - Optical fiber, wireless technologies, and connectivity solutions to carry information and ideas at the speed of light - Precision glass for advanced displays to deliver richer experiences - Auto glass and ceramics to drive cleaner, safer, and smarter transportation - Solar polysilicon, wafers, and innovative photovoltaic modules, enabling low-cost solar energy solutions Location: Remote Role Summary The Data Scientist II / III role is an exciting opportunity to join Corning’s Data Science & Insight (DSI) team, where the individual will develop AI and machine learning solutions that enhance efficiency, generate actionable insights, and improve decision-making across a large and complex Fortune 500 organization. This position sits within the Finance function and supports digital transformation initiatives across both corporate finance and the broader enterprise. A central focus of the role is the design and delivery of enterprise-grade, reusable AI/ML models and frameworks that can be applied across finance to address a broad range of business challenges. The team brings together expertise in statistics, data science, machine learning, artificial intelligence, MLOps, and corporate finance. Projects are executed in a highly collaborative environment, while also requiring strong individual ownership and initiative. Using advanced analytical and modeling techniques, the Data Scientist will enable objective, insight-driven analysis for stakeholders at all levels, including senior leadership. This role requires deep technical capability in applying sophisticated data science and machine learning methods to complex finance-related challenges, including time series analysis, Bayesian modeling, supervised and unsupervised learning, reinforcement learning, deep learning, natural language processing, and Generative AI. The role is responsible for developing scalable, reusable solutions and helping to elevate modeling standards across the finance organization. This position follows a hybrid-remote model, with the expectation of being onsite at Corning headquarters for in-person meetings as needed. Role Context The Data Scientist is a core member of the centralized Digital Center AI team supporting Finance. This role is responsible for building and maintaining shared AI capabilities—including forecasting, predictive modeling, NLP/GenAI, prescriptive analytics, and pattern recognition—for use across FP&A, Treasury, Controllership, Tax, and Risk. Success in this role requires a strong focus on scalability, robustness, and responsible deployment, as well as the consistent application of industry best practices in model development, validation, documentation, governance, and MLOps. The individual in this role is also expected to remain current with advancements in AI and machine learning and translate relevant innovations into practical, enterprise-ready applications. Key Responsibilities - Design, develop, and validate foundational, reusable AI/ML models and frameworks that can be leveraged across multiple finance functions. - Apply advanced statistical and machine learning methods—including time series analysis, Bayesian techniques, tree-based models, clustering, deep learning, NLP, and Generative AI—to solve complex cross-functional finance business problems. - Implement best practices across the full model lifecycle, including problem framing, data quality assessment, feature engineering, validation, interpretability, monitoring, documentation, and reproducibility. - Evaluate existing models, metrics, and workflows critically, and recommend enhancements to improve robustness, scalability, and operational efficiency. - Partner with ML Engineers and Data Engineers to transition prototypes and research into production-ready, governed AI solutions. - Translate analytical findings into clear business insights and recommendations for senior finance leaders and executives. - Coach and mentor embedded Finance data scientists on modeling standards, reusable approaches, and best practices. - Stay informed on emerging AI/ML research, tools, and methodologies, and identify opportunities to adopt innovations that deliver measurable business value and can be operationalized responsibly. - Communicate learnings, model performance, and standards through presentations, documentation, and knowledge-sharing forums. - Compile, integrate, and prepare internal and external data sources for advanced analysis and modeling. - Contribute high-quality, well-documented code to shared repositories in accordance with enterprise standards. Required Education and Experience - Minimum of 5 years of experience applying data science and machine learning methods to solve complex business problems. - Master’s degree or PhD in a quantitative discipline such as Data Science, Statistics, Mathematics, Computer Science, Economics, or Finance. - Academic coursework in applied statistics, machine learning, or data science. - Coursework or demonstrated interest in Finance, Economics, or Operations Management is a plus. Required Qualifications - Demonstrated ability to work independently while contributing effectively within highly collaborative, cross-functional teams. - Proven success in converting research and analytical work into production-ready solutions. - Strong curiosity and willingness to challenge conventional processes and assumptions. - Self-motivated with a commitment to continuous learning and staying current with evolving AI/ML tools and practices. - Ability to communicate complex technical analysis clearly and effectively to senior business stakeholders. - Prior publications or conference presentations in quantitative or technical fields are a plus. Technical Competencies - Strong proficiency in Python and the broader Python AI/data science ecosystem. - Experience with Git-based source control, including platforms such as GitHub or GitLab. - Familiarity with Databricks and cloud-based machine learning platforms such as AWS or Azure is preferred. - Experience with distributed computing frameworks such as Spark is a plus. The range for this position is $109,335.00 - $150,336.00 assuming full time status. Starting pay for the successful applicant is dependent on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education.

• Collaborate with experienced modelers to build predictive models and analytic solutions using Python or R; apply techniques such as GLM, decision trees, GBM and NLP • Manipulate data using R or SQL Server; develop advanced ad hoc queries to investigate data anomalies and to summarize data for pattern detection • Develop Python or R functions and SQL stored procedures to automate recurring tasks • Create and maintain documentation associated with models • Assist in implementation and testing of models • Develop dashboards in Power BI to facilitate analyses that support modeling efforts or enable model usage • Monitor the performance and usage of models

• Analyze complex scientific data using statistics, machine learning, and data science methods • Build and improve workflows for data ingestion, cleaning, feature extraction, modeling, and reporting • Develop AI-enabled tools or workflow automation to support repeatable scientific analysis tasks • Partner with scientists, engineers, and operations teams to solve practical lab data problems • Build, test, validate, and document predictive or classification models for scientific applications • Perform exploratory analysis to identify trends, anomalies, and relationships in experimental data • Support automation and standardization of data analysis and reporting processes • Communicate results clearly to both technical and non-technical stakeholders • Contribute to scalable, reproducible data workflows that improve lab efficiency

Role Description The Accelerator seeks a Data Scientist to bolster our data team and provide insight in the data we collect. This is a 6-month term role with potential for extension. - Work closely with the Accelerator leadership team to align data science initiatives with overarching goals and long-term vision. - Identify and prioritize development projects that benefit from data science methodologies and innovations. - Apply advanced statistical analysis, data mining, and machine learning techniques to analyze complex engineering datasets. - Design, develop, and optimize predictive models, simulations, and algorithms to solve key engineering and scientific challenges. - Drive the development and deployment of data-driven products and solutions, ensuring alignment with strategic goals. - Ensure the accuracy, integrity, and quality of data to be made available through the Accelerator. - Collaborate with data engineering teams to optimize data workflows, enhance data accessibility, and improve performance of modeling systems. - Understand the end-to-end data lifecycle and its effects on machine learning models, actively enhancing and optimizing data pipelines for improved model outcomes. - Develop data pipelines, automation systems, and scalable infrastructure for real-time and batch processing. - Work effectively in a modern, professional software and data engineering environment with a strong understanding of Agile concepts and practices. - Stay current with the latest advancements in data science, machine learning, and relevant engineering fields to continuously innovate. - Collaborate with research teams to apply state-of-the-art techniques to ongoing scientific challenges. - Build strong relationships with external partners, driving collaborations that enhance the Accelerator’s scientific impact. Qualifications - 3+ years of relevant work experience as a frontline data scientist, with a record of building innovative solutions. - Experience working in a remote, agile environment. - Bachelor's degree or equivalent in a relevant field. - Strong communication and interpersonal skills to effectively collaborate with researchers in the field, other engineers at various levels of experience, and administrative and leadership team members. Requirements - Background in engineering principles and familiarity with various engineering disciplines. - Publications in reputable scientific journals or conferences is desirable. Benefits - Comprehensive benefit program to eligible employees.