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About This Role
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Date: Jul 14, 2026
Location: Corning, NY, US, 14831
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Company: Corning
Requisition Number: 74453
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
The Data Scientist role is an exciting opportunity to join Corning’s Data Science \& Insight (DSI) team, building AI/ML solutions for finance to drive efficiency, insight, and better decision\-making across a large and diverse Fortune 500 organization. This position sits within the Finance Function and supports digital transformation across corporate finance and the enterprise. A key focus is designing and delivering enterprise\-grade, reusable AI/ML models and frameworks that can be leveraged across finance to address a wide range of business challenges. The team applies expertise in statistics, data science, machine learning, AI, MLOps, and corporate finance, and projects are executed collaboratively with strong individual ownership.
Using advanced modeling techniques, the Data Scientist enables objective, insightful analysis of stakeholders at all levels, including senior leadership. The role requires strong capability in applying robust data science and machine learning methods to complex finance problems, including time series, Bayesian modeling, supervised and unsupervised learning, reinforcement learning, deep learning, NLP, and GenAI. The candidate will develop scalable, reusable solutions and help advance modeling standards across finance. The position is hybrid\-remote, with the expectation of coming to the Corning HQ office for in\-person meetings as needed.
Role Context
The Data Scientist is a core member of the centralized Digital Center AI team supporting Finance. The role builds and maintains 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, with consistent application of industry best practices in model development, validation, documentation, governance, and MLOps. The Data Scientist is also expected to stay current on AI/ML advancements and translate relevant innovations into practical, enterprise\-ready improvements.
Day to Day 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 techniques (e.g., time series, Bayesian methods, tree\-based models, clustering, deep learning, NLP, GenAI) to solve complex, cross\-finance business problems.
Implement industry best practices across the model lifecycle (problem framing, data quality, feature engineering, validation, interpretability, monitoring, documentation, and reproducibility) to ensure solutions are robust, explainable, and governable.
Critically evaluate existing models, metrics, and workflows; recommend and implement improvements to increase robustness, scalability, and operational efficiency.
Collaborate with ML Engineers and Data Engineers to convert research and prototypes into production\-ready, governed AI solutions.
Translate analytical results into clear insights and recommendations for senior finance leaders and executives.
Coach and mentor embedded and junior data scientists on modeling standards, reusable patterns, and best practices.
Stay current on state\-of\-the\-art AI/ML research and tooling; experiment with emerging methods and drive adoption where they provide clear business value and can be operationalized responsibly.
Share learnings, model performance, and standards through presentations, documentation, and knowledge\-sharing forums.
Compile, integrate, and prepare internal and external datasets for advanced modeling.
Contribute high\-quality, well\-documented code to shared repositories following enterprise standards.
Required Work / Education
Minimum of 5 years of experience applying data science and machine learning methods to solve complex business problems.
MS or PhD in a quantitative discipline (Data Science, Statistics, Mathematics, Computer Science, Economics, Finance).
Coursework in applied statistics, machine learning, or data science.
Coursework or demonstrated interest in Finance, Economics, or Operations Management are a plus.
Required Qualifications
Strong ability to work independently while contributing effectively to highly collaborative, cross\-functional teams.
Proven ability to convert research and analytical work into production\-ready solutions.
Demonstrated curiosity and willingness to challenge traditional processes and assumptions.
Self\-driven with a commitment to continuous learning, including staying current with modern AI/ML practices and tooling.
Ability to present complex technical analysis to senior\-level business stakeholders.
Prior publications or conference presentations in quantitative fields are a plus.
Technical Competencies
Strong proficiency in Python and the Python AI/data science ecosystem.
Experience with Git\-based source control (GitHub, GitLab).
Familiarity with Databricks and cloud ML platforms (AWS, Azure) is a plus.
Experience with distributed computing frameworks (Spark) is a plus
This position supports immigration sponsorship.
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.
A job that shapes a life.
Corning offers you the total package.
Your well\-being is our priority. Our compensation and benefits package supports your health and wellness, financial aspirations, and career from day one.
Company\-wide bonuses and long\-term incentives align with key business results and ensure you are rewarded when the company performs well. When Corning wins, we all win.
As part of our commitment to your financial well\-being, we provide a 100% company\-paid pension benefit with fixed contributions that grow throughout your career. Combined with matching contributions to your 401(k) savings plan, Corning’s total contributions to your retirement accounts can reach between 7% and 12% of your pay, depending on your age and years of service.
Our health and well\-being benefits include medical, dental, vision, paid parental leave, family building support, fitness, company\-paid life insurance, disability, disease management programs, paid time off, and an Employee Assistance Program (EAP) to support you and your family.
Getting paid for our work is important, but feeling appreciated and recognized for those contributions motivates us much more. That’s why Corning offers a recognition program to celebrate successes and reward colleagues who make exceptional contributions.
We prohibit discrimination on the basis of race, color, gender, age, religion, national origin, sexual orientation, gender identity or expression, disability, veteran status or any other legally protected status.
Corning is committed to providing equal employment opportunities and considers requests for reasonable accommodations in accordance with applicable laws. Individuals with disabilities or sincerely held religious beliefs may request reasonable accommodations to participate in the application or interview process, perform essential job functions, or access other benefits and privileges of employment. To submit a request for reasonable accommodation related to disability or religion, please contact us at [email protected].
Nearest Major Market: Corning
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Salary Context
This $109K-$150K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($129K) sits 33% below the category median. Disclosed range: $109K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; 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.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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