Senior Human Resources Data Scientist

Boeing Boeing · Aerospace · USA - Arlington, VA, USA - Seattle, WA, USA - Berkeley, MO, USA - North Charleston, SC

Senior Data Scientist role focused on building and operationalizing HR-specific AI/ML solutions, including predictive models and LLM applications, within a large enterprise. Requires end-to-end project leadership, collaboration with HR stakeholders and engineering teams, and adherence to governance and privacy requirements.

What you'd actually do

  1. Lead end-to-end analytic projects
  2. Define problem statements with HR stakeholders, design experiments, select appropriate methods, develop models, validate results, and deliver production-ready solutions and monitoring
  3. Build predictive and prescriptive models for talent use cases (attrition/retention, internal mobility, promotion forecasting, performance indicators, recruitment sourcing/scoring, skilling/curation, compensation analytics)
  4. Develop and produce features and models in collaboration with data engineers and Machine Learning (ML) engineers
  5. Implement reproducible ETL, feature pipelines, model training pipelines, CI/CD, and deployment patterns

Skills

Required

  • 5+ years of experience in data science or analytics
  • 5+ years of experience performing statistical analysis
  • 5+ years of experience in data analysis algorithms (e.g. data mining, statistics, machine learning, natural language processing, text mining, visual analytics) and building Descriptive, Predictive and Prescriptive models
  • 5+ years of experience writing and using SQL
  • 5+ years of experience in Python or relevant scripting language development
  • Experience in Business Intelligence/data analytics tools (Tableau, Microsoft Power BI, Dashboards, etc.)

Nice to have

  • Masters degree in Data Science, Statistics, Computer Science, Economics, Engineering, or related field
  • Certifications or demonstrable training in responsible AI, ML engineering, or data science best practices (examples: ML engineering certificates, Responsible AI training, cloud certifications)
  • 5+ years of experience delivering end-to-end analytics and deploying models to production in cross-functional environments
  • Experience in Talent/People analytics in a large enterprise or consulting environment
  • Experience in statistical modeling (linear/logistic regression, survival analysis/time-to-event where relevant), tree-based methods, clustering, causal methods, and applied NLP/transformer/LLM techniques for text-based HR applications.
  • Experience working with ETL, feature engineering, data warehouses/lakes, and modern cloud platforms
  • Experience with Spark, dbt, Airflow, or equivalents desirable
  • Experience with model registries and lifecycle tools (MLflow, Seldon, Terraform/Helm or equivalent), explainability tools (SHAP, LIME), fairness/tooling (AIF360 or equivalent), and monitoring frameworks
  • Experience with BI/visualization tools (Tableau, Power BI) and producing executive-ready dashboards and narratives
  • Experience with de-identification, synthetic data, and access-control patterns for sensitive HR data

What the JD emphasized

  • production-grade implementation
  • production-ready solutions
  • deploying models to production
  • operationalize reporting
  • operationalize analytic solutions
  • operationalize NLP/LLM solutions

Other signals

  • operationalize analytic solutions
  • build predictive and prescriptive models
  • implement reproducible ETL, feature pipelines, model training pipelines, CI/CD, and deployment patterns
  • design and operationalize NLP/LLM solutions for HR use cases