Interested in this Data Scientist role at Corning?
Apply Now →Skills & Technologies
About This Role
Date: Jun 9, 2026
Location: Corning, NY, US, 14831
Company: Corning
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.
Nearest Major Market: Corning
Salary Context
This $109K-$150K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Corning, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($129K) sits 34% below the category median. Disclosed range: $109K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Corning AI Hiring
Corning has 1 open AI role right now. They're hiring across Data Scientist. Based in Corning, NY, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.