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About This Role
Date: Apr 10, 2026
Location: Charlotte, NC, US, 28216 Corning, NY, US, 14831
Company: Corning
Requisition Number: 74328
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.
The global Information Technology (IT) Function is leading efforts to align IT and Business Strategy, leverage IT investments, and optimize end to end business processes and associated information integration technologies. Through these efforts, IT helps to improve the competitive position of Corning's businesses through IT enabled processes. IT also delivers Information Technology applications, infrastructure, and project services in a cost efficient manner to Corning worldwide.
Position Overview:
The Senior Data Scientist is a highly skilled, experienced analytical professional responsible for leading complex data science initiatives that drive strategic business decisions and operational improvements. Working within the Optical Communications Data and AI organization, this individual applies advanced statistical modeling, machine learning, experimental design, and data visualization to solve high\-value problems across manufacturing, supply chain, commercial, and technology functions. The Senior Data Scientist operates with a high degree of independence, mentors junior team members, and serves as a trusted analytical partner to business stakeholders.
A Senior Data Scientist in this role will be considered successful when they have demonstrated the following:
- Business Impact \& Value Delivery: Within the first year, successfully contributes to at least two end\-to\-end data science projects that deliver measurable, documented business outcomes \- such as cost reduction, forecast accuracy improvement, or operational efficiency gains \- working collaboratively with senior team members and business stakeholders to validate and communicate results.
- Model Quality \& Growing Technical Independence: Consistently delivers well\-documented, reproducible, and validated analytical models that meet defined performance benchmarks, demonstrating increasing independence in model selection, feature engineering, and validation methodology—while actively incorporating feedback from senior data scientists and machine learning engineers to improve solution quality and production readiness.
- Stakeholder Engagement \& Communication Growth: Demonstrates steady growth in cross\-functional collaboration and communication skills, proactively engaging with business partners to understand requirements, presenting analytical findings clearly and confidently to technical and non\-technical audiences, and earning recognition as a reliable and developing analytical contributor within assigned business functions.
Key Responsibilities:
- Advanced Analytics \& Modeling
- + Design and execute sophisticated analytical solutions, including predictive modeling, statistical analysis, machine learning, and hypothesis\-driven research, to address complex business challenges.
+ Apply domain expertise to frame, scope, and deliver high\-impact data science projects with minimal supervision.
+ Lead exploratory data analysis, feature engineering, and model selection for a wide range of business use cases including demand forecasting, quality improvement, pricing, and operational efficiency.
- Business Partnership
- + Collaborate closely with business leaders and cross\-functional stakeholders to identify opportunities, define analytical requirements, and translate data insights into clear, actionable recommendations.
+ Present findings and recommendations confidently to executive and non\-technical audiences, emphasizing business impact and decision relevance.
+ Build strong, trust\-based relationships with technology, commercial, supply chain, and manufacturing partners.
- Team Leadership \& Mentorship
- + Provide technical mentorship and guidance to junior data scientists and analysts, supporting their professional development and analytical skill growth.
+ Lead and contribute to knowledge\-sharing sessions, best practice development, and internal training initiatives.
+ Participate actively in project reviews, peer feedback, and collaborative problem\-solving across the data science team.
- Innovation \& Continuous Improvement
- + Proactively identify and evaluate emerging analytical methods, tools, and technologies relevant to the business.
+ Champion best practices in reproducibility, documentation, version control, and model governance.
+ Contribute to the development and refinement of data science standards and workflows across the organization.
- Collaboration with Engineering
- + Work closely with machine learning engineers to ensure models are properly validated, documented, and transitioned into production environments.
+ Support model monitoring, performance evaluation, and iterative improvement in deployed solutions.
Qualifications:
- Bachelor’s or Master's degree in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, or a related quantitative discipline; PhD a plus.
- 3\+ years of progressive experience applying data science techniques to real\-world business problems in an industrial, manufacturing, or technology environment.
- Advanced proficiency in Python and/or R, including experience with key libraries (pandas, scikit\-learn, NumPy, TensorFlow, PyTorch, etc.).
- Deep expertise in statistical modeling, predictive analytics, supervised and unsupervised machine learning, and experimental design.
- Strong experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) and the ability to communicate complex findings visually.
- Proven track record of independently managing and delivering complex analytical projects with measurable business outcomes.
- Experience working with large, complex datasets from diverse sources (e.g., ERP, MES, IoT, CRM, etc.).
- Familiarity with cloud platforms (AWS, Azure, GCP) and big data technologies is a plus.
- Strong communication, stakeholder management, and presentation skills.
Desired Qualifications:
- Experience with Databricks, including working within the Databricks Lakehouse Platform for collaborative data science, large\-scale data processing, model training, and MLflow\-integrated model lifecycle management.
- Familiarity with Apache Spark and distributed computing concepts within the Databricks environment.
- Experience using Databricks notebooks for exploratory analysis, feature engineering, and model development in a collaborative team setting.
- Highly analytical, intellectually curious, and motivated by solving complex, ambiguous problems.
- Confident self\-starter who takes ownership and delivers results with minimal supervision.
- Collaborative mentor and team contributor who elevates those around them.
- Strong business acumen and ability to connect analytical work to tangible organizational value.
- Adaptable and eager to continuously learn in a fast\-paced, innovative environment.
This position does not support immigration sponsorship.
The range for this position is $115,046\.00 \- $158,189\.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.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. To request an accommodation, please contact us at accommodations@corning.com.
Nearest Major Market: Charlotte
Salary Context
This $115K-$158K range is below the median for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% 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 $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($136K) sits 33% below the category median. Disclosed range: $115K to $158K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Corning AI Hiring
Corning has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Charlotte, NC, US, Wilmington, NC, US. Compensation range: $112K - $158K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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