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[Remote] Data Scientist (Remote)

Remote · USA Full-time New today

Note: The job is a remote job and is open to candidates in USA. Corning Incorporated is a leading innovator in glass, ceramic, and materials science. They are seeking a Data Scientist to join their Data Science & Insight team, focusing on developing AI and machine learning solutions to enhance efficiency and decision-making within the finance function. The role involves designing and delivering enterprise-grade AI/ML models to solve complex business challenges across the organization.

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

Skills

  • 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
  • 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
  • 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
  • Coursework or demonstrated interest in Finance, Economics, or Operations Management is a plus
  • 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
  • Prior publications or conference presentations in quantitative or technical fields are a plus

Company Overview

  • Corning is a manufacturer of glass, ceramics, and related materials. It was founded in 1851, and is headquartered in Corning, New York, USA, with a workforce of 10001+ employees. Its website is https://www.corning.com/.
  • Company H1B Sponsorship

  • Corning Incorporated has a track record of offering H1B sponsorships, with 6 in 2026, 50 in 2025, 27 in 2024, 35 in 2023, 46 in 2022, 70 in 2021, 45 in 2020. Please note that this does not guarantee sponsorship for this specific role.
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