HR Data Analyst

Key Responsibilities:

1. Data Collection and Analysis: Collect, clean, and analyze HR data from various sources, including HRIS (Human Resources Information Systems), performance management systems, employee surveys, and other relevant databases.
2. Metrics Development: Develop and maintain key HR metrics and KPIs (Key Performance Indicators) to track and measure workforce trends, employee engagement, retention, turnover, diversity, performance, and other relevant HR metrics.
3. Trend Identification: Identify patterns, trends, and correlations within HR data to provide insights into employee behavior, performance drivers, and organizational dynamics.
4. Predictive Analytics: Utilize statistical modeling and predictive analytics techniques to forecast future workforce trends, attrition rates, talent gaps, and other critical HR metrics.
5. Dashboard Development: Design and create dashboards and data visualizations to present HR metrics and insights in a clear, concise, and actionable format for HR leadership and other stakeholders.
6. Ad Hoc Analysis: Conduct ad hoc analysis and deep dives into specific HR issues or initiatives, providing actionable recommendations to HR leadership based on data-driven insights.
7. Data Integrity and Compliance: Ensure the accuracy, integrity, and security of HR data, adhering to relevant data privacy regulations and best practices in data management.
8. Collaboration: Collaborate cross-functionally with HR business partners, talent acquisition teams, compensation and benefits specialists, and other stakeholders to leverage HR data for strategic decision-making and process improvement initiatives.

Qualifications:

1. Bachelor's degree in HR, Business Administration, Statistics, Data Science, or a related field (Master's degree preferred).
2. 2 years of experience working with HRIS - SAP SuccessFactors.
3. 5 years of experience in HR analytics, data analysis, or a related role.
4. Proficiency in data analysis tools and programming languages such as Excel, SQL, Python, R, or similar tools.
5. Strong analytical and problem-solving skills, with the ability to translate complex data into actionable insights.
6. Excellent communication and presentation skills, with the ability to convey technical information to non-technical stakeholders.
7. Detail-oriented with a commitment to data accuracy and quality.