The distribution of workers across jobs and across different geographic areas has major implications for growth, social welfare and inequality. It is complex but essential to understand the mechanisms behind the allocation of talents in the economy, that is, how skilled individuals choose their jobs and where they live. However, the existing literature on the distribution of skills faces two major challenges. First, it has largely neglected the role of the marriage market and family constraints, although family formation and partner choice are intimately linked to career and location choices and these choices influence each other. Second, it must go beyond the standard one-dimensional classification of skills based on educational attainment alone. This hierarchy underestimates the inequalities that exist between multidimensional and non-hierarchical skill sets. SkiM2Lab will address this challenge in developing state-of-the-art multidimensional matching models with two specific objectives. In the first objective, I will analyze the interactions between the labor market and the marriage market using equilibrium models of matching where individuals and jobs are associated with multidimensional skill sets and are located in different places. Estimating these structural models on household data will reveal how family and labor markets affect wage disparities and occupational segregation by gender and region. In the second objective, I will leverage big data such as online resumes and online job postings, as well as machine learning and natural language processing technologies, to extract skills at the most granular level, build new relevant combinations of skills and include them in a competitive matching model. This will make it possible to propose a new method to identify rapidly relevant emerging skills and their impact on wages and production.