Senior Data Scientist

Weyerhaeuser•Seattle, WA

6d•$108,521 - $162,782•Onsite

About The Position

Grasp the opportunity to apply data science to the physical world of manufacturing! We are looking for a Senior Data Scientist 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 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. The position is part of our Data Science team and will be located in Seattle, WA.

Requirements

5+ years of experience solving problems through statistical modeling, applied machine learning, and data analysis.
Strong proficiency in programming environments and languages, specifically Python (e.g. pandas, scikit-learn, etc.) and R.
Deep understanding of machine learning, statistical modeling, time series forecasting, optimization, anomaly detection, and experimentation methodologies.
Experience applying machine learning and deep learning techniques using frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
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 Databricks.
Experience working with large datasets of manufacturing, quality, and sensor/IoT data.
Excellent problem-solving skills and ability to translate business problems into data science solutions.
Communicate findings clearly to both technical and non-technical stakeholders, providing recommendations that drive measurable improvements.

Nice To Haves

Practical experience with Snowflake.
Practical experience in Forestry Services or Wood Product manufacturing
Experience with Industrial Internet of Things and time-series manufacturing data

Responsibilities

Collaborate with manufacturing engineers, maintenance teams, and operations leaders to understand the challenges and opportunities of our manufacturing processes.
Analyze manufacturing process data, sensor/IoT data, and quality metrics to uncover actionable insights.
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.
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.