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# Data Scientist

- **Company:** Weyerhaeuser
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Data Architecture, Decision Support Systems, Python (Programming Language), Machine Learning, Power BI, Tensorflow, Software Engineering, Statistical Process Control (SPC), Enterprise Data Management, Supervised Learning, Feature Engineering, Snowflake, Matplotlib, Data Analytics, Plotly, Machine Learning Operations
- **Published:** August 11, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/data-scientist-98101-seattle-wa-usa-58894934

## About the Role

_ and contribute to analytics libraries. Tasks * 5+ years in developing and deploying ML/AI solutions in manufacturing or related domains. * Strong software engineering skills in Python and modern ML frameworks. * Expertise in supervised learning, forecasting, optimization, statistical modeling, anomaly detection, evaluation, and experimentation. * Proven success delivering enterprise-scale AI products from concept to production. * Experience leading ambiguous technical initiatives. * Ability to influence technical strategy across teams. * Experience with experimentation and causal inference including A/B testing and counterfactual analysis. * Experience communicating insights with Power BI or Python visualization (Plotly/Matplotlib). * Experience with cloud platforms and data architectures (AWS, Azure, Snowflake) and MLOps, CI/CD, lifecycle management. Key requirements * comprehensive benefits plan * medical, dental, vision * short and long-term disability * life insurance * 401k aa

* company match * paid vacation and holidays

## Description

Experteer Overview As a Data Scientist at Weyerhaeuser, you will apply advanced analytics to manufacturing, aiming to improve product quality, reliability, and mill uptime. You'll partner with mills and cross-functional teams to translate problems into ML opportunities and define measurable business outcomes. Your work spans time-series data, sensors, and enterprise data to drive data-driven decisions and operational improvements. This role combines scientific rigor with practical impact in a sustainability-driven, industrial setting. You will shape analytics practices and contribute to scalable AI solutions across manufacturing domains. Compensation / Benefits * Collaborate with manufacturing, reliability, maintenance, quality, and operations teams to translate problems into machine learning opportunities. * Analyze large industrial time-series and sensor data to identify patterns, bottlenecks, and root causes. * Establish reusable patterns, standards, and best practices for model development and deployment. * Define success metrics balancing model performance with business outcomes such as efficiency and quality. * Partner with Product Managers to identify and prioritize opportunities solvable with a scientific approach. * Influence technical direction across programs without direct authority. * Design, execute, and analyze experiments (A/B testing, causal inference) to evaluate impact. * Develop and evaluate ML/DL models for forecasting, optimization, reliability, anomaly detection, and decision support. * Implement statistical process control and anomaly detection to address quality issues. * Own end-to-end model lifecycle: feature engineering, training, validation, deployment, monitoring, retraining, improvement. * Collaborate with engineers to productionize models and integrate AI into workflows. * Translate ambiguous problems into scientific approaches and communicate data-driven recommendations. * Develop visualizations and dashboards to drive business decisions and contribute to analytics libraries. Tasks * 5+ years in developing and deploying ML/AI solutions in manufacturing or related domains. * Strong software engineering skills in Python and modern ML frameworks. * Expertise in supervised learning, forecasting, optimization, statistical modeling, anomaly detection, evaluation, and experimentation. * Proven success delivering enterprise-scale AI products from concept to production. * Experience leading ambiguous technical initiatives. * Ability to influence technical strategy across teams. * Experience with experimentation and causal inference including A/B testing and counterfactual analysis. * Experience communicating insights with Power BI or Python visualization (Plotly/Matplotlib). * Experience with cloud platforms and data architectures (AWS, Azure, Snowflake) and MLOps, CI/CD, lifecycle management. Key requirements * comprehensive benefits plan * medical, dental, vision * short and long-term disability * life insurance * 401k with company match * paid vacation and holidays


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