Manager, Data and Analytics

Location:

Everett, WA (3 days in office)

The Manager, Data and Analytics leads the data and analytics team within the CIO organization, with accountability for delivering trusted data products, reporting, and AI models that inform measurable business decisions.

This role manages the analytics and AI modeling backlog, the datasets and pipelines those products depend on, and the day-to-day delivery practices of a team of analysts, data scientists, and data engineers.

The manager partners closely with business stakeholders and with platform and engineering teams to translate business questions into analytics and modeling work, and to ensure models are validated, monitored, and used responsibly in line with enterprise governance.

• Lead a team of data analysts, data scientists, and data engineers, including hiring, coaching, performance management, and capability development.
• Deliver business productivity via AI models forecasting, propensity, risk, demand, and anomaly detection from problem framing through validation, deployment, and ongoing monitoring.
• Own the analytics and AI modeling roadmap for assigned business areas, sequencing work against stakeholder priorities and available team capacity.
• Translate business questions into analytical problems, defining hypotheses, success criteria, and the data required to answer them.
• Manage intake and prioritization of analytics and modeling requests, including feasibility assessment, effort sizing, and expected business benefit.
• Build and maintain trusted datasets, pipelines, and metric definitions that support reporting, self-service analytics, and model features.
• Own data quality and documentation for the team's data products, ensuring consistent metrics across reporting and models.
• Deliver decision-ready dashboards and self-service reporting, retiring redundant reports and eliminating duplicate metric definitions.
• Establish standard work for the analytics lifecycle, including version control, code review, model documentation, testing, and reproducibility.
• Monitor deployed models for accuracy, drift, and business impact, and retrain or retire models as conditions change.
• Partner with Security, Privacy, and Legal so that data use, model use, and access controls follow enterprise policy and responsible AI standards.
• Manage tooling and vendor usage within the team's approved footprint and provide input into platform and licensing decisions owned by the platform organization.
• Provide clear status, risk, and results reporting to senior leadership, including value delivered and delivery KPIs such as on-time delivery, cycle time, model performance, and adoption.
• Define what is out of scope and hold the line (e.g., no modeling work without a named business owner and a defined decision it will inform; no production models without documentation and monitoring).

People-management role with direct accountability for the delivery of analytics and AI modeling work, the capability of the team, and the quality of the data products the team owns.

Balances fast, exploratory analysis with the rigor required for production models and trusted reporting.

• 5+ years of experience in data analytics, data science, or AI modeling, including leading or managing a team.
• Hands-on depth in statistical and machine learning methods for prediction (regression, classification, time-series forecasting, clustering) and in evaluating model performance.
• Strong SQL plus Python or R, with working experience in modern data warehouse and pipeline tooling.
• Demonstrated experience taking AI models into production and monitoring them for accuracy, drift, and business impact.
• Experience with BI and visualization tools and building decision-ready reporting for business audiences.
• Proven ability to scope ambiguous business questions into deliverable analytical work and to communicate results to non-technical stakeholders.
• Working knowledge of data governance, privacy, and responsible AI expectations as applied to data access and model development.
• Bachelor's degree in a quantitative field, or equivalent practical experience.

• AI models and analytics products are adopted by business owners and demonstrably inform decisions, with quantified value where measurable.
• Deployed models stay accurate and monitored, with documented ownership, performance thresholds, and retraining cadence.
• Data owned by the team is trusted, documented, and consistent across reporting and models,…