Requisition Number: 74528
Location: Remote
Role SummaryThe 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 ContextThe 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.
