Principal Data Scientist

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 Principal 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. This role is responsible for shaping AI and ML strategy, defining reusable machine learning capabilities, leading complex experimentation, and delivering scalable solutions that create measurable business value. 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

• Lead the design of scalable solutions across multiple business domains.

• Establish reusable patterns, standards, and best practices for model development and deployment.

• Lead and develop advanced machine learning, optimization, forecasting, generative AI, and decision intelligence solutions. 

• 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. 

• Establish standards for feature stores, model registries, inference services, observability, and governance. 

• Collaborate with software engineers, ML engineers, and data engineers to productionize models and integrate AI capabilities into business workflows. 

• Demonstrate the ability to apply data science and machine learning techniques across multiple domains (e.g., manufacturing, supply chain, pricing, logistics), abstracting core patterns, and adapting solutions to new problem spaces. 

• 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. 

• Mentor junior team members and contribute to data science standards, reusable patterns, and best practices.