Industrial Data Scientist: Manufacturing Analytics & AI

Weyerhaeuser; Seattle (WA); On-site; USD 99,000 - 148,000; Full time

Job summary

Weyerhaeuser in Seattle is seeking a Data Scientist to apply machine learning and statistics to manufacturing, reliability, and supply chain. You will work with historian data, MES, sensors, and ERP to build models that improve product quality, equipment uptime, and operational efficiency.

You will lead end-to-end model lifecycle from feature engineering to deployment, collaborate with software and data engineers, and present findings through dashboards and presentations to business leaders.

Qualifications

• 5+ years of experience developing and deploying machine learning and AI solutions in manufacturing, industrial, or related domains.
• Strong software engineering skills in Python and modern ML frameworks.
• Experience with experimentation and causal inference methods, including A/B testing and counterfactual analysis.
• Experience communicating insights using Power BI or Python-based visualization libraries such as Plotly and Matplotlib.

Responsibilities

• Partner with manufacturing, reliability, maintenance, quality, and operations teams to understand business problems and translate them into machine learning opportunities.
• Analyze industrial time-series, historian, MES, ERP, and sensor data to identify patterns, bottlenecks, and root causes.
• Design, deploy, and monitor ML models end-to-end including lifecycle management.
• Collaborate with engineers to productionize models and integrate AI into workflows.
• Develop dashboards and visualizations to drive business decisions.

Skills

Python

ML frameworks

Supervised learning

Forecasting

Optimization

Experimentation

Anomaly detection

Data visualization

Education

Bachelor's or Master's in Data Science

Tools

Plotly

Matplotlib

AWS

Azure

Snowflake

CI/CD

MLOps

Power BI

Job description

Weyerhaeuser in Seattle is seeking a Data Scientist to apply machine learning and statistics to manufacturing, reliability, and supply chain. You will work with historian data, MES, sensors, and ERP to build models that improve product quality, equipment uptime, and operational efficiency.

You will lead end-to-end model lifecycle from feature engineering to deployment, collaborate with software and data engineers, and present findings through dashboards and presentations to business leaders.