Data Scientist

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. We recognize that our success is dependent on the success of our people. For over 125 years, our team has been making a difference in the world—from the seedlings we plant to the forests and trees we nurture.

About the role

We are seeking an experienced Data Scientist to provide technical leadership and apply machine learning, statistics, experimentation, and optimization techniques to solve complex business problems across manufacturing, operations reliability, supply chain, and product quality domains. You will partner with our manufacturing mills to identify, analyze, and solve problems related to production quality, equipment reliability, and preventative maintenance, directly impacting operational efficiency, 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 improve mill performance. This role requires a high attention to detail, big-picture thinking, and the ability to champion ideas while effectively communicating with 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 Operations teams to identify, prioritize, and frame business opportunities solvable with a 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.
• 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.

Requirements

• 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 learning, 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 Qualifications

• Practical experience with Recommendation Systems, Pricing Optimization, and Computer Vision.
• Experience in Forestry Services or Wood Product manufacturing.
• Experience with Industrial Internet of Things and time-series manufacturing data.

Qualifications

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or related quantitative discipline.

Pay

This role is eligible for our annual merit-increase program, with a targeted salary range of $108,521–$162,782 based on skills, qualifications, and experience. You will also be eligible for our Annual Incentive Program, which offers a cash bonus targeting 15% of base pay, with potential plan funding ranging from zero to two times that target.

Benefits

• Comprehensive employee benefits plan, including medical, dental, vision, short- and long-term disability, and life insurance.
• Pre-tax Health Savings Account option with a company contribution.
• Voluntary Long-Term Care and Employee Assistance Programs.
• Support for personal volunteerism, diversity networks, mentoring, and training and development opportunities.
• 401k plan with a company match and an additional contribution equal to 5% of eligible pay.
• 3 weeks of paid vacation during the first year of employment, accruing additional vacation after six months.
• 11 paid holidays per year (88 holiday hours) and paid parental leave for all full-time employees.

Schedule

Full-time, day shift (1st). Relocation assistance available.