Process Data Scientist: Manufacturing Analytics & AI

Corning, Inc.; Keller (TX); On-site; USD 91,000 - 126,000; Full time

Benefits offered by this job

Company bonuses

401(k) employer match

Comprehensive health benefits

Paid parental leave

Job summary

Corning, Inc. is seeking a Process Data Scientist to partner with process engineering and manufacturing teams, applying statistics, ML, and AI to improve process understanding, quality, yield, and cost.

You will work with MES, ERP, and QC data, building scalable analyses and communicating results to technical and non-technical stakeholders. The role involves designing experiments, predictive modeling, anomaly detection, and applying AI capabilities in a manufacturing setting.

Qualifications

• Bachelor’s degree in a data-related field is required.
• Minimum 3+ years of experience in data science, statistics, or a related field, preferably in manufacturing or industrial settings.
• Proficiency in Python and SQL with experience on big data platforms (Databricks, Spark, Hadoop).

Responsibilities

• Frame business and technical questions as well-defined analytical problems with cross-functional teams.
• Plan and analyze designed experiments, SPC, measurement system analyses, hypotheses tests, and other statistics methods.
• Develop predictive and prescriptive models using ML and statistical techniques for anomaly detection, maintenance, throughput, and quality.
• Explore AI capabilities including computer vision and generative AI in manufacturing contexts.
• Create dashboards, reports, and technical summaries communicating findings to stakeholders.

Skills

Python

SQL

Statistics

Machine Learning

Data Visualization

Education

Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field

Tools

Databricks

Spark

Hadoop

Job description

Corning, Inc. is seeking a Process Data Scientist to partner with process engineering and manufacturing teams, applying statistics, ML, and AI to improve process understanding, quality, yield, and cost.

You will work with MES, ERP, and QC data, building scalable analyses and communicating results to technical and non-technical stakeholders. The role involves designing experiments, predictive modeling, anomaly detection, and applying AI capabilities in a manufacturing setting.