Note: The job is a remote job and is open to candidates in USA. Corning Incorporated is one of the world’s leading innovators in glass, ceramic, and materials science. They are seeking a Data Scientist to develop AI and machine learning solutions that enhance efficiency and improve decision-making within the finance function of a large 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
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