Senior Business Intelligence and Governance Architect

Boeing Boeing · Aerospace · Seattle, WA +4

This role focuses on governing AI/ML models within Human Resources at Boeing. It involves defining policies, reviewing datasets and models for accuracy, fairness, and compliance, and monitoring post-deployment performance. The role acts as a bridge between data science, IT, legal, and business leaders to ensure responsible AI use.

What you'd actually do

  1. Define, document, and enforce HR data governance policies, data definitions, metadata, and lineage
  2. Monitor, measure, and improve data quality (completeness, accuracy, consistency, timeliness, uniqueness); investigate root causes and coordinate remediation
  3. Participate in AI/ML model governance lifecycle: intake, review, approval, monitoring, and decommissioning for HR models
  4. Review datasets used for training/validation: confirm representativeness, label quality, feature provenance, and linkage to canonical HR records
  5. Evaluate model inputs and outputs for appropriateness, interpretability, and the absence of impermissible/sensitive attributes unless explicitly justified and approved

Skills

Required

  • 10+ years of experience in human resources operations, human resources information systems, human resources analytics, or data governance
  • 10+ years of experience in Business Intelligence/data analytics tools (Microsoft Power BI, Dashboards, SQL, Tableau, etc.)
  • 5+ years of experience with SQL and Graph databases
  • 5+ years of experience with privacy-preserving techniques (de-identification, synthetic data, access controls)

Nice to have

  • Bachelor’s degree in Human Resources, Information Management, Data Science, Business, or related field (or equivalent experience)
  • Experience interpreting model and fairness metrics and translate technical findings into business risk and mitigation actions
  • Experience with AI/ML model governance or model review responsibilities
  • Experience with ML lifecycle concepts, model registries, and explainability/fairness tools (examples: MLflow, model registries, SHAP, LIME, AIF360)
  • Strong analytical, problem-solving, and documentation skills

What the JD emphasized

  • responsible use of Artificial Intelligence (AI) / Machine Learning (ML) in Human Resources (HR)
  • review models
  • accurate, fair, explainable, and compliant
  • AI/ML model governance lifecycle
  • Evaluate model inputs and outputs for appropriateness, interpretability, and the absence of impermissible/sensitive attributes unless explicitly justified and approved
  • Define and apply evaluation criteria and metrics relevant to HR (accuracy, calibration, precision/recall, and fairness measures such as demographic parity or equalized odds) and interpret their operational implications
  • Ensure explainability, human-in-the-loop controls, and decision-review processes for models influencing hiring, promotion, compensation, performance management, or disciplinary actions
  • Monitor post-deployment performance and drift; trigger alerts, remediation, or retraining when thresholds are exceeded
  • Enforce data minimization and privacy-preserving techniques (de-identification, synthetic data, access controls) for model training and inference
  • Maintain or review audit trails for model predictions and decision logs to support investigations and compliance requests
  • Coordinate model risk reviews and approvals with Ethics/AI governance, Legal/Privacy, Security, and HR leadership
  • Experience with AI/ML model governance or model review responsibilities
  • Experience interpreting model and fairness metrics and translate technical findings into business risk and mitigation actions

Other signals

  • AI/ML model governance lifecycle
  • Review datasets for training/validation
  • Evaluate model inputs and outputs for fairness and explainability
  • Define and apply evaluation criteria and metrics
  • Monitor post-deployment performance and drift