.Identify & conduct Global HR analytics: Descriptive, Diagnostic, Predictive & Prescriptive (incl. AI)- Coordinate Global HR Analytics Network (CoE, OHR & Regions)- Provide transparency on best practices & use cases (Int. & Ext.)**Main Responsibilities**:- Identify and prioritize global challenges and use cases for HR Analytics- Approach to identify and prioritize opportunities where Analytics bring value: Descriptive, Diagnostic, Predictive & Prescriptive (including use of Artificial Intelligence)- Translate business needs into HR analytics requirements (Data Translator between HR & Data Science team)- Create and maintain updated a Global Roadmap of HR Analytics initiatives- Stay on top of trends and solutions- Work with stakeholders to develop questions and hypotheses to provide insight into HR matters- Develop HR Analytics solutions to support HR teams in decision-making & interactions with their stakeholders- Conduct complex data analysis, and identify patterns and trends- Advance local use of Artificial Intelligence in HR (Predictive, Descriptive, Machine Learning, and Gen AI)- Responsible for designing and developing advanced business intelligence (BI) solutions that enable organizations to extract insights and value from their data. **(e.G. Headcount, KPI's)**:- Activate HR Analytics Network- Generate the space to share best practices, needs, challenges and ideas- Coordinate Global HR Analytics Network (CoE, OHR & Regions)- Provide transparency on best practices & use cases (Int. & Ext.)**Position Challenges**:- Understanding the various aspects of HR that can benefit from analytics, such as recruitment, employee engagement, performance management, and retention strategies. It requires a deep understanding of HR processes and the ability to identify where analytics can add value.- Collaboration with Centers of Excellence, Operational HR, and regional teams to ensure alignment and sharing of best practices and use cases.- Providing transparency on best practices and use cases: Maintain a clear and open channel of communication regarding the successes and learnings from analytics initiatives to foster a culture of continuous improvement and innovation.**Qualifications**:- **Languages needed & proficiency**:- **Spanish and English**: Advanced proficiency, with the ability to communicate effectively in a business environment.- **Academic Background**: A bachelor's degree in fields such as Computer Systems, Digital Transformation, Data Science or Engineering. Master in Data Analytics- **Areas of Expertise**: 5-7 years in HR data management, data analysis, or a related field.- **Technical Skills**:- **Programming Languages**:Proficiency in Python- **Data Management and Visualization**: Experience with SQL for database management, and tools like Power BI or Tableau for data visualization