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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Weyerhaeuser - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $108,521.0 - $162,782.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Continuous Integration, Data Architecture, Python (Programming Language), Machine Learning, Recommender Systems, Reliability Engineering, Power BI, Tensorflow, Software Engineering, Statistical Process Control (SPC), Visual Systems, Feature Engineering, Snowflake, Model Validation, Matplotlib, Information Technology, Optimization Algorithms, Data Analytics, Plotly, Machine Learning Operations - **Published:** August 11, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/27922029/Data-Scientist-Washington-Seattle-1314 ## About the Role * 5 years of experience developing and deploying machine learning and AI solutions in manufacturing, industrial, supply chain, or related domains. * Strong software engineering skills in Python and modern ML frameworks. * Expertise in supervised learing, forecasting, optimization, statistical modeling, anomaly detection, model evaluation and experimentation methodologies. * Demonstrated success delivering enterprise-scale AI products from concept through production. * Experience leading highly ambiguous technical initiatives. * Proven ability to influence technical strategy across multiple teams and organizations. * Experience with experimentation and causal inference methods, including A/B testing, quasi-experimental designs, and counterfactual analysis. * Experience communicating insights using Power BI or Python-based visualization libraries such as Plotly and Matplotlib. * Experience with modern cloud platforms and data architectures, including AWS, Azure, Snowflake, and MLOps, CI/CD, and model lifecycle management. Preferred, not required: * Practical experience with Recommendation Systems, Pricing Optimization, and Computer Vision * Practical experience in Forestry Services or Wood Product manufacturing * Experience with Industrial Internet of Things and time-series manufacturing data Education * Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or related quantitative discipline. ## Description Grasp the opportunity to apply data science to the physical world of manufacturing! We are seeking an experienced Data Scientist to provide technical leadership and passionate about applying machine learning, statistics, experimentation, and optimization techniques to solve complex business problems across manufacturing, operations reliability, supply chain, and product quality domains. We have a large manufacturing presence in North America with lumber, OSB, plywood, and engineered lumber products mills in Canada and the United States. Our Weyerhaeuser brand and scale of operations make us a major player in the wood products business. You would be partnering with our manufacturing mills to identify, analyze, and solve complex problems related to production quality, equipment reliability, and preventative maintenance. Your work would directly impact operational efficiency, improved product quality, and mill uptime. You will work with historian data, MES systems, machine sensors, vision systems, operational events, and enterprise data to build solutions that directly impact mill performance. You have a high attention to detail, but are good at seeing the big picture, and aren't afraid to think outside the box, and champion your ideas. You have experience articulating opportunity, as well as creating and successfully managing projects. You are effective at communicating timely and relevant information to business leaders and internal partners. Responsibilities * Partner with manufacturing, reliability, maintenance, quality, and operations teams to understand business problems and translate them into machine learning opportunities. * Analyze large volumes of industrial time-series, historian, MES, ERP, 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 that balance model performance with business outcomes including revenue growth, operational efficiency, customer experience, safety, and risk reduction. * Partner with Product Managers and Operation teams to identify, prioritize, and frame business opportunities that can be solved with scientific framework. * Influence technical direction across multiple programs without direct authority. * Design, execute, and analyze online and offline experiments, including A/B testing, causal inference, and counterfactual analysis, to evaluate the impact of data science solutions on business outcomes. * Design, develop, and evaluate machine learning and deep learning models to solve forecasting, optimization, reliability, anomaly detection, and decision-support problems. * Design and implement statistical process control methods and anomaly detection techniques to proactively address quality issues in the manufacturing process. * Own the end-to-end model lifecycle, including feature engineering, training, validation, deployment, monitoring, retraining, and continuous improvement. * Collaborate with software engineers, ML engineers, and data engineers to productionize models and integrate AI capabilities into business workflows. * Translate ambiguous business problems into scientific approaches and influence stakeholders through data-driven recommendations. * Develop analytical visualizations and communicate findings through dashboards, notebooks, and presentations that drive business decisions. * Contribute to reusable analytics libraries, feature engineering patterns, and best practices across Industrial AI use cases. ## Related Videos - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Solving the puzzle: Leveraging machine learning for effective root cause analysis](https://www.wearedevelopers.com/videos/1518-solving-the-puzzle-leveraging-machine-learning-for-effective-root-cause-analysis) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)