Principal Data Scientist

Salary: $131,082 - 196,766 per year Requirements:

• We require 10+ years of experience building and deploying machine learning and AI solutions.
• We need strong software engineering capability in Python and modern machine learning frameworks.
• We expect deep expertise in supervised learning, forecasting, optimization, statistical modeling, anomaly detection, model evaluation, and experimentation methods.
• We look for a proven track record of delivering enterprise-scale AI products from concept through production.
• We value experience leading complex, ambiguous technical initiatives.
• We need demonstrated ability to shape technical strategy across multiple teams and organizations.
• We require hands-on experience with experimentation and causal inference, including A/B testing, quasi-experimental designs, and counterfactual analysis.
• We expect experience presenting insights with Power BI or Python visualization tools such as Plotly and Matplotlib.
• We look for experience with cloud platforms and data architectures, including AWS, Azure, Snowflake, MLOps, CI/CD, and model lifecycle management.
• Preferred, but not required: experience with recommendation systems, pricing optimization, and computer vision.
• Preferred, but not required: experience in forestry services or wood products manufacturing.
• Preferred, but not required: experience with industrial Internet of Things and time-series manufacturing data.
• We require a PhD in Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or a related quantitative field, or a masters degree with equivalent industry experience.

• We lead the design of scalable solutions across multiple business areas.
• We establish reusable patterns, standards, and best practices for model development and deployment.
• We build and guide advanced machine learning, optimization, forecasting, generative AI, and decision intelligence solutions.
• We define success measures that align model performance with revenue growth, operational efficiency, customer experience, safety, and risk reduction.
• We partner with product managers and operations teams to identify, prioritize, and frame business opportunities through a scientific approach.
• We influence technical direction across multiple programs without direct authority.
• We design, run, and interpret online and offline experiments, including A/B testing, causal inference, and counterfactual analysis.
• We design, develop, and assess machine learning and deep learning models for forecasting, optimization, reliability, anomaly detection, and decision support.
• We implement statistical process control and anomaly detection methods to address quality issues proactively in manufacturing.
• We own the full model lifecycle, including feature engineering, training, validation, deployment, monitoring, retraining, and continuous improvement.
• We establish standards for feature stores, model registries, inference services, observability, and governance.
• We collaborate with software engineers, ML engineers, and data engineers to productionize models and embed AI into business workflows.
• We apply data science and machine learning across domains such as manufacturing, supply chain, pricing, and logistics, adapting core patterns to new problem spaces.
• We translate ambiguous business challenges into scientific methods and influence stakeholders with data-driven recommendations.
• We create analytical visualizations and communicate insights through dashboards, notebooks, and presentations that support decision-making.
• We mentor junior team members and help advance data science standards, reusable patterns, and best practices.

• AI
• AWS
• Azure
• CI/CD
• Cloud
• Computer Vision
• Support
• Machine Learning
• Power BI
• Python
• Snowflake

More:

We are Weyerhaeuser, the worlds premier timberland and forest products company, with a long-standing commitment to sustainability that has guided our business since 1938. For more than 125 years, our team has cared for forests and supported a large manufacturing footprint across North America, producing lumber, OSB, plywood, and engineered lumber in Canada and the United States. In this Principal Data Scientist role, we apply data science to real-world manufacturing challenges that improve quality, reliability, maintenance, and mill uptime while shaping AI and ML strategy across the business. We offer a competitive compensation package with eligibility for annual merit increases and incentive pay, comprehensive health and insurance benefits, retirement savings with company match and contribution, paid vacation and holidays, parental leave, volunteer and mentoring support, and training and development opportunities. We are an equal opportunity employer and are committed to building an inclusive workplace where our people feel belonging, opportunity, and shared purpose.

last updated 34 week of 2026