• Build predictive models to forecast trends such as employee turnover, workforce engagement, and talent needs. • Develop prescriptive analytics solutions to inform HR strategies and optimize decision-making processes. • Analyze large datasets from various systems (e.g., Workday, Greenhouse) to uncover actionable insights. • Implement machine learning frameworks for solving HR-related challenges. • Harness insights from unstructured or qualitative data sources, such as performance reviews, annual engagement survey comments, or open-text survey responses. • Develop natural language processing (NLP) models to analyze text data and uncover trends, sentiment, or organizational themes. • Collaborate with stakeholders to define business problems, translate them into technical solutions, and communicate results effectively. • Integrate predictive outputs into Snowflake or visualization platforms like Tableau and Looker. • Partner with HR and data engineering teams to ensure seamless data pipeline management and integrity. • Maintain a strong focus on data accuracy, security, and compliance.