Principal Data Scientist

The Principal Data Scientist leads AI and ML initiatives across manufacturing, reliability, and supply chain at onsite Seattle, WA, shaping strategy and guiding the end-to-end model lifecycle while partnering with mills to improve quality and uptime. The role carries a salary range of USD 131,082 – 196,766 per year.

Responsibilities

• Design scalable solutions that span 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 connect model performance to business outcomes including revenue growth, operational efficiency, customer experience, safety, and risk reduction.
• Collaborate with Product Managers and Operations teams to identify, prioritize, and frame opportunities solvable with a scientific framework.
• Influence technical direction across multiple programs without direct authority.
• Plan, execute, and analyze online and offline experiments, including A/B testing, causal inference, and counterfactual analysis, to evaluate impact on business outcomes.
• Design, develop, and evaluate machine learning and deep learning models to address forecasting, optimization, reliability, anomaly detection, and decision-support problems.
• Develop statistical process control methods and anomaly detection techniques to proactively address manufacturing quality issues.
• 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.
• Apply data science and ML techniques across domains such as manufacturing, supply chain, pricing, and logistics, extracting 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 via dashboards, notebooks, and presentations to drive decisions.
• Mentor junior team members and contribute to data science standards, reusable patterns, and best practices.

Requirements

• 10+ years of experience developing and deploying machine learning and AI solutions.
• Strong software engineering skills in Python and modern ML frameworks.
• Deep expertise in supervised learing, 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.

Technologies

• Python
• Power BI
• Plotly
• Matplotlib
• AWS
• Azure
• Snowflake
• MLOps
• CI/CD

Benefits

• Medical, dental, vision, short- and long-term disability, and life insurance.
• Health Savings Account option with company contribution.
• Voluntary Long-Term Care and Employee Assistance Programs.
• Support for personal volunteerism, sponsor diversity networks, mentoring, and training and development opportunities.
• 401(k) plan with company match and 5% employer contribution.
• Paid time off: 3 weeks vacation in the first year plus accrual after six months.
• Eleven paid holidays per year (88 hours) and paid parental leave.

Education

• PhD in Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or related quantitative discipline; or Master’s degree with equivalent industry experience.