Lead Data Scientist developing AI/ML solutions for Disney's media supply chain. Collaborating globally to drive innovation in complex media challenges.
About the role
- HR Data Scientist advancing KLA’s HR Analytics capability through AI and predictive insights. Collaborating with HR partners to provide data-driven decision-making support.
Responsibilities
- 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
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