Partner with HRBPs, COEs, and HR leadership to frame business questions, define hypotheses, and translate needs into analytics solutions across descriptive, predictive, and prescriptive use cases
Analyze workforce data using statistical and analytical methods to identify key patterns, drivers, and relationships (e.g., engagement, attrition, mobility, hiring outcomes)
Design, build, validate, and maintain predictive models (e.g., attrition risk, internal mobility, workforce demand, workforce planning, TA funnel outcomes), including feature engineering, evaluation, and ongoing monitoring
Design and build AI analytic tools to help with the acceleration of data insights
Own end‑to‑end analytics delivery for more advanced analytics use cases from data exploration and modelling through insight generation, storytelling, and clear recommendations for decision‑makers
Develop reusable analytical assets (model templates, code libraries, documented methodologies, metric definitions) to enable scalable and repeatable analytics across HR
Ensure data integrity and reliability by auditing datasets, diagnosing issues, and implementing data quality checks and controls
Collaborate closely with HR Data Engineering and IT to improve datasets, pipelines, and the overall analytics foundation (Workday/Prism and other Analytics platforms and external sources)
Establish and follow data privacy, ethics, and governance practices for employee data, including appropriate use, fairness and bias considerations, transparency, and access control
Contribute to the HR analytics roadmap by identifying high‑value predictive use cases and opportunities to automate insight delivery

Requirements

Bachelor’s degree in a quantitative or analytical field (e.g., Statistics, Math, Economics, Engineering, Data Science) or equivalent practical experience
2+ years of experience in data science or advanced analytics roles, with direct experience working on HR / people / workforce analytics (e.g., attrition, engagement, mobility, hiring, workforce planning)
Hands‑on experience applying machine learning and statistical techniques to people‑related business problems, including supervised learning, feature engineering, and model evaluation
Demonstrated ability to translate HR business questions into analytical and predictive solutions, working closely with HR stakeholders
Experience working with complex, imperfect HR data and exercising sound analytical judgment around assumptions, limitations, bias, and uncertainty
Proficiency in Python or R, with the ability to communicate insights and recommendations clearly to non‑technical HR and business audiences
Working knowledge of data privacy, ethics, and responsible use of employee data
Experience applying tree‑based machine learning models such as Random Forest and XGBoost to people‑related analytics use cases (e.g., attrition risk, mobility, hiring outcomes)
Experience or familiarity with unsupervised learning techniques (e.g., clustering, segmentation, anomaly detection) to explore workforce patterns and inform hypothesis generation
Familiarity with model explainability and governance in an HR context (e.g., feature importance, bias/fairness considerations, documentation, and model monitoring)
Experience working with Workday data or HR systems (e.g., Workday reporting or Prism Analytics) is a plus
Working knowledge of SQL for data exploration and validation is a plus
Experience collaborating with data engineering to productionize models and analytics is a plus
Copilot and other AI experience

Benefits

medical, dental, vision, life, and other voluntary benefits
401(K) including company matching
employee stock purchase program (ESPP)
student debt assistance
tuition reimbursement program
development and career growth opportunities and programs
financial planning benefits
wellness benefits including an employee assistance program (EAP)
paid time off and paid company holidays
family care and bonding leave

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data analysispredictive modelingmachine learningstatistical techniquesfeature engineeringmodel evaluationSQLunsupervised learningtree-based modelsdata quality checks

Soft Skills

analytical judgmentcommunicationcollaborationstorytellingdecision-making