• Use SQL, Python, or other analytical tools to query, clean, and analyze large datasets. • Analyze labor market trends across dimensions such as workforce composition, job demand, employment patterns, and wage dynamics. • Apply advanced techniques including time series forecasting, clustering, regression analysis, and machine learning algorithms (e.g., decision trees, random forests, gradient boosting, support vector machines, k-means clustering) to identify patterns and forecast labor trends. • Build and maintain scalable data and model pipelines using orchestration tools like Apache Airflow, Luigi, or Sagemaker Pipelines, and model tracking tools such as MLflow. • Collaborate cross-functionally to define problem statements, gather and analyze data, integrate models into products, and translate insights into actionable recommendations for technical and non-technical audiences. • Stay informed on advancements in labor market analytics, statistical modeling, and machine learning technologies.