Lean Six Sigma projects and the quality engineering profession have to deal with an extensive selection of tools most of them requiring specialized training. The increased availability of standard statistical software motivates the use of advanced data science techniques to identify relationships between potential causes and project metrics. In these circumstances, Symbolic Regression has received increased attention from researchers and practitioners to uncover the intrinsic relationships hidden within complex data without requiring specialized training for its implementation. The objective of this paper is to evaluate the advantages and drawbacks of using computer assisted Symbolic Regression within the Analyze phase of a Lean Six Sigma project. An application of this approach in a service industry project is also presented.