Data Scientist-01024736

Description About Weyerhaeuser At Weyerhaeuser, we are the world's premier timberland, and forest products company. Sustainability is the founding concept of our business, and our values drive every decision to ensure we continue to lead the forestry industry in sustainability practices. And we know about sustainability - we led it in the forestry industry when we planted our first seedling by hand in 1938. We recognize that our success is dependent on the success of our people. For over 125 years, our Weyerhaeuser team has been making a difference in the world - from the seedlings we plant, to the forests and trees we nurture, we ensure every acre is managed with diligence, patience and pride. That's the Weyerhaeuser way. About the Role 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.