Industrial Data Scientist: ML for Smart Manufacturing

RiseMe

Seattle (WA)

Hybrid

USD 99,000 - 148,000

Full time

5 days ago

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Benefits offered by this job

Health benefits

401k with company match

Paid vacation

Paid holidays

Parental leave

Job summary

Weyerhaeuser in North America seeks a Data Scientist to apply machine learning, statistics, and optimization to manufacturing, reliability, and supply chain challenges across mills in the U.S. and Canada.

You will partner with manufacturing teams to identify opportunities, analyze historian data, MES, ERP, and sensors, build scalable models, and deploy solutions that improve product quality, reliability, and mill uptime. Excellent communication and collaboration are essential.

Qualifications

5+ years of experience developing and deploying ML/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.

Responsibilities

Partner with manufacturing 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.