Senior Human Resources Data Scientist

Boeing•Arlington, VA

8d•$204,000 - $295,550•Onsite

About The Position

The Boeing Company’s Talent Analytics and Innovations team is currently seeking a Senior Human Resources (HR) Data Scientist to join our team in Seattle, WA, Arlington, VA, North Charleston, SC or Berkeley, MO. As a Data Scientist on the Talent Analytics team you will design, build, and operationalize analytic solutions that solve workforce and HR challenges and deliver measurable business impact. You will partner with HR leaders, HR Information Systems (HRIS)/Information Technology(IT), data engineering, and analytics to translate business questions into reproducible data products, end-to-end pipelines, and interpretable models (including ML and Large Language Model-based solutions). This is a hands-on role that combines advanced analytics, production-grade implementation, and stakeholder-facing communication in a large-organization HR context.

Requirements

Bachelor's degree or higher
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 Haves

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

Responsibilities

Lead end-to-end analytic projects
Define problem statements with HR stakeholders, design experiments, select appropriate methods, develop models, validate results, and deliver production-ready solutions and monitoring
Build predictive and prescriptive models for talent use cases (attrition/retention, internal mobility, promotion forecasting, performance indicators, recruitment sourcing/scoring, skilling/curation, compensation analytics)
Develop and produce features and models in collaboration with data engineers and Machine Learning (ML) engineers
Implement reproducible ETL, feature pipelines, model training pipelines, CI/CD, and deployment patterns
Apply statistical methods, hypothesis testing, causal inference where appropriate, and robust validation (cross-validation, holdouts, calibration, fairness testing) to ensure reliable, defensible results
Design and operationalize NLP/LLM solutions for HR use cases (resume parsing, candidate experience, employee feedback analysis) while enforcing privacy, data minimization and explainability requirements
Instrument model monitoring and drift detection; define alerting, retraining triggers, and remediation plans
Produce clear, actionable visualizations and dashboards that tell the story of analytic findings and drive decisions; collaborate with BI developers to operationalize reporting
Translate technical analyses into business recommendations, quantify expected impact, and work with partners to implement changes and measure outcomes
Mentor junior data scientists/analysts, review code and model artifacts, and help raise team standards for reproducibility, documentation, and governance
Ensure models and data products adhere to governance, privacy, and ethical requirements; collaborate with HR Data Steward, Legal/Privacy, and Ethics/AI governance on reviews and approvals