Build and deploy predictive and time-series models using HR and clinical data (Oracle HCM, GCP) to forecast staffing, identify flight-risk employees, analyze pay/burnout relationships, and create interactive dashboards and analyses to inform HR strategy and operational workforce decisions.
Job Summary
This role is responsible for aligning the enterprise talent management strategy with operational data; building visualizations, statistical analyses, and predictive models to track progress and uncover areas for improvement. From forecasting nurse attrition to optimizing recruitment spend and ensuring pay equity, this role will use data sourced from Oracle HCM and other operational and clinical systems to drive the delivery of a best-in-class healthcare workforce.
Essential Functions
- Collaborate with cross-functional teams including Human Resources (HR) and other operational leaders, data scientists, and software engineers to identify data-driven opportunities for enhancing human capital and operational processes.
- Utilize cloud-based technologies, such as Google Cloud Platform (GCP), for scalable data processing and analysis.
- Serve as the "Data Translator" for HR leadership, converting high-level people strategies (e.g., "reducing turnover by 15%") into specific analytical projects and KPIs.
- Utilize Python programming and associated libraries such as PyTorch, Keras, Pandas and NumPY to create, train, test and implement meaningful data science models.
- Create time-series models to predict future staffing needs based on seasonal patient volume, historical turnover, and local labor market trends
- Develop and deploy machine learning models to identify "flight risk" employees, allowing leadership to intervene before high-value talent exits the organization.
- Lead the analysis of multifactorial relationships, including those between premium pay (travelers/overtime) and employee burnout to provide recommendations on sustainable staffing and retainment models.
- Collaborate with healthcare professionals and domain experts to understand operational needs and design data-driven solutions.
- Design and conduct experiments, interpret results, and communicate findings to both technical and non-technical stakeholders
Qualifications
- Bachelor's Degree in Data Science, Computer Science, Bioinformatics, or a related field required
- Master's Degree preferred
- 5-7 years of experience in healthcare data engineering and analytics in a clinical or biomedical research setting required
- Proficiency in Python programming and associated libraries.
- Experience with cloud-based platforms such as Google Cloud Platform (GCP) for data storage, processing, and deployment.
- 3–5 years of experience in People Analytics or HR Data Science, preferably within a large, complex organization (5,000+ employees).
- Proven experience extracting and manipulating data from Oracle HCM Cloud, Workday, or SAP SuccessFactors.
- History of translating ambiguous business questions into rigorous SQL logic.
- Proficiency Advanced proficiency in modern visualization suites (e.g., Tableau, Power BI, Looker) and programmatic libraries (e.g., Matplotlib, Seaborn, Plotly), with demonstrated experience creating interactive executive dashboards to granular technical deep dives.
- Understanding of core HR functions: Talent Acquisition, Total Rewards, Employee Relations, and Learning & Development.
- Deep understanding of data privacy laws (GDPR, CCPA) and the ethical implications of using AI in the employee lifecycle
- Excellent communication and presentation skills with the ability to translate technical concepts to non-technical audiences.
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